Year |
Citation |
Score |
2024 |
Liu S, Oh SK, Pedrycz W, Yang B, Wang L, Seo K. Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis. Ieee Transactions On Cybernetics. PMID 38416627 DOI: 10.1109/TCYB.2024.3353753 |
0.365 |
|
2023 |
Zhang T, Zhang Y, Ma F, Peng C, Yue D, Pedrycz W. Local Boundary Fuzzified Rough K -Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularity. Ieee Transactions On Cybernetics. PMID 37030830 DOI: 10.1109/TCYB.2023.3257274 |
0.319 |
|
2022 |
Zhang C, Oh SK, Fu Z, Pedrycz W. Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models. Ieee Transactions On Cybernetics. PMID 37015632 DOI: 10.1109/TCYB.2022.3228303 |
0.374 |
|
2022 |
Liu P, Li Y, Zhang X, Pedrycz W. A Multiattribute Group Decision-Making Method With Probabilistic Linguistic Information Based on an Adaptive Consensus Reaching Model and Evidential Reasoning. Ieee Transactions On Cybernetics. PMID 35486566 DOI: 10.1109/TCYB.2022.3165030 |
0.353 |
|
2022 |
Pedrycz W. Computing and Clustering in the Environment of Order-2 Information Granules. Ieee Transactions On Cybernetics. PMID 35427227 DOI: 10.1109/TCYB.2022.3163350 |
0.354 |
|
2021 |
Zhu X, Wang D, Pedrycz W, Li Z. A Design of Granular Classifier Based on Granular Data Descriptors. Ieee Transactions On Cybernetics. PMID 34936563 DOI: 10.1109/TCYB.2021.3132636 |
0.354 |
|
2021 |
Lu W, Ma C, Pedrycz W, Yang J. Design of Granular Model: A Method Driven by Hyper-Box Iteration Granulation. Ieee Transactions On Cybernetics. PMID 34767519 DOI: 10.1109/TCYB.2021.3124235 |
0.341 |
|
2021 |
Hu X, Shen Y, Pedrycz W, Li Y, Wu G. Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation. Ieee Transactions On Cybernetics. PMID 33909582 DOI: 10.1109/TCYB.2021.3071145 |
0.387 |
|
2021 |
Hu X, Shen Y, Pedrycz W, Wang X, Gacek A, Liu B. Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering. Ieee Transactions On Cybernetics. PMID 33878000 DOI: 10.1109/TCYB.2021.3069783 |
0.377 |
|
2021 |
Liang Y, Ju Y, Qin J, Pedrycz W. Multi-granular linguistic distribution evidential reasoning method for renewable energy project risk assessment Information Fusion. 65: 147-164. DOI: 10.1016/J.Inffus.2020.08.010 |
0.32 |
|
2020 |
Zhu X, Pedrycz W, Li Z. A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models. Ieee Transactions On Cybernetics. PMID 33151886 DOI: 10.1109/TCYB.2020.3025668 |
0.301 |
|
2020 |
Zhu X, Pedrycz W, Li Z. A Two-Stage Approach for Constructing Type-2 Information Granules. Ieee Transactions On Cybernetics. PMID 32721903 DOI: 10.1109/Tcyb.2020.2965967 |
0.428 |
|
2020 |
Tang M, Liao H, Herrera-Viedma E, Chen CLP, Pedrycz W. A Dynamic Adaptive Subgroup-to-Subgroup Compatibility-Based Conflict Detection and Resolution Model for Multicriteria Large-Scale Group Decision Making. Ieee Transactions On Cybernetics. PMID 32149679 DOI: 10.1109/Tcyb.2020.2974924 |
0.385 |
|
2020 |
Tan A, Shi S, Wu WZ, Li J, Pedrycz W. Granularity and Entropy of Intuitionistic Fuzzy Information and Their Applications. Ieee Transactions On Cybernetics. PMID 32142467 DOI: 10.1109/Tcyb.2020.2973379 |
0.408 |
|
2020 |
Guo H, Wang L, Liu X, Pedrycz W. Information Granulation-Based Fuzzy Clustering of Time Series. Ieee Transactions On Cybernetics. PMID 32112690 DOI: 10.1109/Tcyb.2020.2970455 |
0.418 |
|
2020 |
Han Z, Pedrycz W, Zhao J, Wang W. Hierarchical Granular Computing-Based Model and Its Reinforcement Structural Learning for Construction of Long-Term Prediction Intervals. Ieee Transactions On Cybernetics. PMID 32011274 DOI: 10.1109/Tcyb.2020.2964011 |
0.352 |
|
2020 |
Wu Y, Dong Y, Qin J, Pedrycz W. Linguistic Distribution and Priority-Based Approximation to Linguistic Preference Relations With Flexible Linguistic Expressions in Decision Making. Ieee Transactions On Cybernetics. PMID 31995508 DOI: 10.1109/Tcyb.2019.2953307 |
0.344 |
|
2020 |
Meng FY, Tang J, Pedrycz W, An QX. Optimal Interaction Priority Calculation From Hesitant Fuzzy Preference Relations Based on the Monte Carlo Simulation Method for the Acceptable Consistency and Consensus. Ieee Transactions On Cybernetics. PMID 31945009 DOI: 10.1109/Tcyb.2019.2962095 |
0.447 |
|
2020 |
Tian G, Hao N, Zhou M, Pedrycz W, Zhang C, Ma F, Li Z. Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices Ieee Transactions On Systems, Man, and Cybernetics. 1-14. DOI: 10.1109/Tsmc.2019.2906635 |
0.468 |
|
2020 |
Du S, Wu M, Chen L, Zhou K, Hu J, Cao W, Pedrycz W. A Fuzzy Control Strategy of Burn-Through Point Based on the Feature Extraction of Time-Series Trend for Iron Ore Sintering Process Ieee Transactions On Industrial Informatics. 16: 2357-2368. DOI: 10.1109/Tii.2019.2935030 |
0.384 |
|
2020 |
Zhang B, Pedrycz W, Wang X, Gacek A. Design of Interval Type-2 Information Granules Based on the Principle of Justifiable Granularity Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3023758 |
0.344 |
|
2020 |
Zhang C, Oh S, Fu Z, Pedrycz W. Design of Reinforced Hybrid Fuzzy Rule-based Neural Networks Driven to Inhomogeneous Neurons and Tournament Selection Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3018190 |
0.377 |
|
2020 |
Zhang B, Li C, Dong Y, Pedrycz W. A Comparative Study Between Analytic Hierarchy Process and Its Fuzzy Variants: A Perspective based on Two Linguistic Models Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3018110 |
0.396 |
|
2020 |
Bui Q, Vo B, Snasel V, Pedrycz W, Hong TP, Nguyen N, Chen M. SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3014662 |
0.423 |
|
2020 |
Mamaghani AS, Pedrycz W. Genetic-Programming based Architecture of Fuzzy Modeling: Towards Coping with High-dimensional Data Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3006993 |
0.378 |
|
2020 |
Zhang B, Dong Y, Feng X, Pedrycz W. Maximum Fuzzy Consensus Feedback Mechanism with Minimum Cost and Private Interest in Group Decision Making Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3006559 |
0.361 |
|
2020 |
Gao D, Wang G, Pedrycz W. Solving Fuzzy Job-shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3003506 |
0.371 |
|
2020 |
Liu P, wang P, Pedrycz W. Consistency- and consensus-based group decision-making method with incomplete probabilistic linguistic preference relations Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3003501 |
0.307 |
|
2020 |
Yang C, Oh S, Pedrycz W, Fu Z, Yang B. Design of Reinforced Fuzzy Radial Basis Function Neural Networks Classifier Driven with the Aid of Iterative Learning Techniques and Support Vector-based Clustering Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.3001740 |
0.409 |
|
2020 |
Lu W, Pedrycz W, Yang J, Liu X. Granular Description with Multi-Granularity for Multidimensional Data: A Cone-Shaped Fuzzy Set-Based Method Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2985335 |
0.487 |
|
2020 |
Cui Y, E H, Pedrycz W, Li Z. Designing Distributed Fuzzy Rule-Based Models Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2984971 |
0.415 |
|
2020 |
Liu F, Huang M, Pedrycz W, Zhao H. Group decision making based on flexibility degree of fuzzy numbers under a confidence level Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2983663 |
0.397 |
|
2020 |
Ding W, Pedrycz W, Lin C. Guest Editorial for the Special Issue on Fuzzy Rough Sets for Big Data Ieee Transactions On Fuzzy Systems. 28: 803-805. DOI: 10.1109/Tfuzz.2020.2979204 |
0.438 |
|
2020 |
Ding W, Pedrycz W, Triguero I, Cao Z, Lin C. Multigranulation Super-Trust Model for Attribute Reduction Ieee Transactions On Fuzzy Systems. 1-14. DOI: 10.1109/Tfuzz.2020.2975152 |
0.495 |
|
2020 |
Zhang B, Pedrycz W, Fayek AR, Gacek A, Dong Y. Granular Aggregation of Fuzzy Rule-Based Models in Distributed Data Environment Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2973956 |
0.416 |
|
2020 |
Gupta P, Mehlawat MK, Khaitan A, Pedrycz W. Sentiment Analysis for Driver Selection in Fuzzy Capacitated Vehicle Routing Problem with Simultaneous Pick-up and Drop in Shared Transportation Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2970834 |
0.353 |
|
2020 |
Yang X, Yu F, Pedrycz W. Typical Characteristics-based Type-2 Fuzzy C-Means Algorithm Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2969907 |
0.402 |
|
2020 |
Liu Y, Zhao J, Wang D, Pedrycz W. Prediction Intervals for Granular Data Streams Based on Evolving Type-2 Fuzzy Granular Neural Network Dynamic Ensemble Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2020.2966172 |
0.404 |
|
2020 |
Chen L, Su W, Wu M, Pedrycz W, Hirota K. A Fuzzy Deep Neural Network With Sparse Autoencoder for Emotional Intention Understanding in Human–Robot Interaction Ieee Transactions On Fuzzy Systems. 28: 1252-1264. DOI: 10.1109/Tfuzz.2020.2966167 |
0.346 |
|
2020 |
Mehlawat MK, Gupta P, Khaitan A, Pedrycz W. A Hybrid Intelligent Approach to Integrated Fuzzy Multiple Depot Capacitated Green Vehicle Routing Problem With Split Delivery and Vehicle Selection Ieee Transactions On Fuzzy Systems. 28: 1155-1166. DOI: 10.1109/Tfuzz.2019.2946110 |
0.415 |
|
2020 |
Pratama M, Pedrycz W, Webb GI. An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams Ieee Transactions On Fuzzy Systems. 28: 1315-1328. DOI: 10.1109/Tfuzz.2019.2939993 |
0.413 |
|
2020 |
Zhang Z, Wu C, Pedrycz W. A Novel Group Decision-Making Method for Interval-Valued Intuitionistic Multiplicative Preference Relations Ieee Transactions On Fuzzy Systems. 28: 1799-1814. DOI: 10.1109/Tfuzz.2019.2922917 |
0.379 |
|
2020 |
Kim E, Oh S, Pedrycz W, Fu Z. Reinforced Fuzzy Clustering-Based Ensemble Neural Networks Ieee Transactions On Fuzzy Systems. 28: 569-582. DOI: 10.1109/Tfuzz.2019.2911492 |
0.414 |
|
2020 |
Ping X, Pedrycz W. Output Feedback Model Predictive Control of Interval Type-2 T–S Fuzzy System With Bounded Disturbance Ieee Transactions On Fuzzy Systems. 28: 148-162. DOI: 10.1109/Tfuzz.2019.2900844 |
0.325 |
|
2020 |
Rong M, Gong D, Pedrycz W, Wang L. A Multimodel Prediction Method for Dynamic Multiobjective Evolutionary Optimization Ieee Transactions On Evolutionary Computation. 24: 290-304. DOI: 10.1109/Tevc.2019.2925358 |
0.301 |
|
2020 |
Wu M, Su W, Chen L, Pedrycz W, Hirota K. Two-stage Fuzzy Fusion based-Convolution Neural Network for Dynamic Emotion Recognition Ieee Transactions On Affective Computing. 1-1. DOI: 10.1109/Taffc.2020.2966440 |
0.349 |
|
2020 |
Wang H, Li K, Pedrycz W. An Elite Hybrid Metaheuristic Optimization Algorithm for Maximizing Wireless Sensor Networks Lifetime With a Sink Node Ieee Sensors Journal. 20: 5634-5649. DOI: 10.1109/Jsen.2020.2971035 |
0.303 |
|
2020 |
Wang L, Han Z, Pedrycz W, Zhao J, Wang W. A Granular Computing-Based Hybrid Hierarchical Method for Construction of Long-Term Prediction Intervals for Gaseous System of Steel Industry Ieee Access. 8: 63538-63550. DOI: 10.1109/Access.2020.2983446 |
0.348 |
|
2020 |
Singla M, Ghosh D, Shukla KK, Pedrycz W. Robust twin support vector regression based on rescaled Hinge loss Pattern Recognition. 105: 107395. DOI: 10.1016/J.Patcog.2020.107395 |
0.37 |
|
2020 |
Zhang C, Oh S, Co ZF, Pedrycz W. Self-organized Hybrid Fuzzy Neural Networks Driven with the Aid of Probability-based Node Selection and Enhanced Input Strategy Neurocomputing. DOI: 10.1016/J.Neucom.2020.08.072 |
0.422 |
|
2020 |
Colace F, Loia V, Pedrycz W, Tomasiello S. On a granular functional link network for classification Neurocomputing. 398: 108-116. DOI: 10.1016/J.Neucom.2020.02.090 |
0.362 |
|
2020 |
Karczmarek P, Kiersztyn A, Pedrycz W, Al E. K-Means-based isolation forest Knowledge Based Systems. 195: 105659. DOI: 10.1016/J.Knosys.2020.105659 |
0.334 |
|
2020 |
Liu F, Zhang J, Zhang W, Pedrycz W. Decision making with a sequential modeling of pairwise comparison process Knowledge Based Systems. 195: 105642. DOI: 10.1016/J.Knosys.2020.105642 |
0.358 |
|
2020 |
Fu C, Lu W, Pedrycz W, Yang J. Rule-based granular classification: A hypersphere information granule-based method Knowledge-Based Systems. 194: 105500. DOI: 10.1016/J.Knosys.2020.105500 |
0.378 |
|
2020 |
Li L, Pedrycz W, Qu T, Li Z. Fuzzy associative memories with autoencoding mechanisms Knowledge Based Systems. 191: 105090. DOI: 10.1016/J.Knosys.2019.105090 |
0.36 |
|
2020 |
Tang X, Peng Z, Zhang Q, Pedrycz W, Yang S. Consistency and consensus-driven models to personalize individual semantics of linguistic terms for supporting group decision making with distribution linguistic preference relations Knowledge Based Systems. 189: 105078. DOI: 10.1016/J.Knosys.2019.105078 |
0.391 |
|
2020 |
Zhang Y, Miao D, Pedrycz W, Zhao T, Xu J, Yu Y. Granular structure-based incremental updating for multi-label classification Knowledge Based Systems. 189: 105066. DOI: 10.1016/J.Knosys.2019.105066 |
0.316 |
|
2020 |
Aliev R, Pedrycz W, Guirimov B, Huseynov O. Clustering method for production of Z-number based if-then rules Information Sciences. 520: 155-176. DOI: 10.1016/J.Ins.2020.02.002 |
0.485 |
|
2020 |
Meng T, Jing X, Yan Z, Pedrycz W. A survey on machine learning for data fusion Information Fusion. 57: 115-129. DOI: 10.1016/J.Inffus.2019.12.001 |
0.303 |
|
2020 |
Ghosh D, Debnath AK, Pedrycz W. A variable and a fixed ordering of intervals and their application in optimization with interval-valued functions International Journal of Approximate Reasoning. 121: 187-205. DOI: 10.1016/J.Ijar.2020.03.004 |
0.334 |
|
2020 |
Xu S, Ju H, Shang L, Pedrycz W, Yang X, Li C. Label distribution learning: A local collaborative mechanism International Journal of Approximate Reasoning. 121: 59-84. DOI: 10.1016/J.Ijar.2020.02.003 |
0.331 |
|
2020 |
Yun U, Nam H, Kim J, Kim H, Baek Y, Lee J, Yoon E, Truong TC, Vo B, Pedrycz W. Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases Future Generation Computer Systems. 103: 58-78. DOI: 10.1016/J.Future.2019.09.024 |
0.351 |
|
2020 |
Zhou J, Pedrycz W, Gao C, Lai Z, Yue X. Principles for constructing three-way approximations of fuzzy sets: A comparative evaluation based on unsupervised learning Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2020.06.019 |
0.495 |
|
2020 |
Yang C, Oh S, Yang B, Pedrycz W, Fu Z. Fuzzy quasi-linear SVM classifier: Design and analysis Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2020.05.010 |
0.446 |
|
2020 |
Xu K, Pedrycz W, Li Z, Nie W. Optimizing the prototypes with a novel data weighting algorithm for enhancing the classification performance of fuzzy clustering Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2020.05.009 |
0.389 |
|
2020 |
Kerr-Wilson J, Pedrycz W. Generating a hierarchical fuzzy rule-based model Fuzzy Sets and Systems. 381: 124-139. DOI: 10.1016/J.Fss.2019.07.013 |
0.483 |
|
2020 |
Mencar C, Pedrycz W. Granular counting of uncertain data Fuzzy Sets and Systems. 387: 108-126. DOI: 10.1016/J.Fss.2019.04.018 |
0.362 |
|
2020 |
Nguyen D, Luo W, Vo B, Pedrycz W. Succinct Contrast Sets via False Positive Controlling with an Application in Clinical Process Redesign Expert Systems With Applications. 161: 113670. DOI: 10.1016/J.Eswa.2020.113670 |
0.402 |
|
2020 |
Mamaghani AS, Pedrycz W. Structural optimization of fuzzy rule-based models: Towards efficient complexity management Expert Systems With Applications. 152: 113362. DOI: 10.1016/J.Eswa.2020.113362 |
0.483 |
|
2020 |
Chen Z, Zhang X, Pedrycz W, Wang X, Skibniewski MJ. Bid evaluation in civil construction under uncertainty: A two-stage LSP-ELECTRE III-based approach Engineering Applications of Artificial Intelligence. 94: 103835. DOI: 10.1016/J.Engappai.2020.103835 |
0.425 |
|
2020 |
Zhang B, Dong Y, Zhang H, Pedrycz W. Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory European Journal of Operational Research. 287: 546-559. DOI: 10.1016/J.Ejor.2020.04.014 |
0.304 |
|
2020 |
Du S, Wu M, Chen L, Cao W, Pedrycz W. Operating mode recognition of iron ore sintering process based on the clustering of time series data Control Engineering Practice. 96: 104297. DOI: 10.1016/J.Conengprac.2020.104297 |
0.317 |
|
2020 |
Chen X, Peng L, Wu Z, Pedrycz W. Controlling the worst consistency index for hesitant fuzzy linguistic preference relations in consensus optimization models Computers & Industrial Engineering. 143: 106423. DOI: 10.1016/J.Cie.2020.106423 |
0.45 |
|
2020 |
Yu D, Xu Z, Pedrycz W. Bibliometric analysis of rough sets research Applied Soft Computing. 94: 106467. DOI: 10.1016/J.Asoc.2020.106467 |
0.302 |
|
2020 |
Ghanbari M, Allahviranloo T, Pedrycz W. On the rectangular fuzzy complex linear systems Applied Soft Computing. 91: 106196. DOI: 10.1016/J.Asoc.2020.106196 |
0.432 |
|
2020 |
Qin J, Xi Y, Pedrycz W. Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method Applied Soft Computing. 89: 106134. DOI: 10.1016/J.Asoc.2020.106134 |
0.31 |
|
2020 |
Tang X, Zhang Q, Peng Z, Pedrycz W, Yang S. Distribution linguistic preference relations with incomplete symbolic proportions for group decision making Applied Soft Computing. 88: 106005. DOI: 10.1016/J.Asoc.2019.106005 |
0.358 |
|
2020 |
Cabrerizo FJ, Al-Hmouz R, Morfeq A, Martínez MÁ, Pedrycz W, Herrera-Viedma E. Estimating incomplete information in group decision making: A framework of granular computing Applied Soft Computing. 86: 105930. DOI: 10.1016/J.Asoc.2019.105930 |
0.412 |
|
2020 |
Tang Y, Ren F, Pedrycz W. Fuzzy C-Means clustering through SSIM and patch for image segmentation Applied Soft Computing. 87: 105928. DOI: 10.1016/J.Asoc.2019.105928 |
0.37 |
|
2020 |
Alfaro-García VG, Merigó JM, Pedrycz W, Monge RG. Citation Analysis of Fuzzy Set Theory Journals: Bibliometric Insights About Authors and Research Areas International Journal of Fuzzy Systems. 1-35. DOI: 10.1007/S40815-020-00924-8 |
0.322 |
|
2020 |
Balamash A, Pedrycz W, Al-Hmouz R, Morfeq A. Data Description Through Information Granules: A Multiview Perspective International Journal of Fuzzy Systems. 1-17. DOI: 10.1007/S40815-020-00903-Z |
0.4 |
|
2020 |
Liu F, Huang M, Huang C, Pedrycz W. Measuring consistency of interval-valued preference relations: comments and comparison Operational Research. 1-29. DOI: 10.1007/S12351-020-00551-Z |
0.375 |
|
2020 |
Liu F, Zhang J, Yu Q, Peng Y, Pedrycz W. On weak consistency of interval additive reciprocal matrices Fuzzy Optimization and Decision Making. 19: 153-175. DOI: 10.1007/S10700-020-09314-Z |
0.349 |
|
2020 |
Jin J, Ye M, Pedrycz W. Quintuple Implication Principle on interval-valued intuitionistic fuzzy sets Soft Computing. 24: 12091-12109. DOI: 10.1007/s00500-019-04649-1 |
0.321 |
|
2020 |
Liu P, Xu H, Pedrycz W. A normal wiggly hesitant fuzzy linguistic projection‐based multiattributive border approximation area comparison method International Journal of Intelligent Systems. 35: 432-469. DOI: 10.1002/Int.22213 |
0.405 |
|
2019 |
Zhu X, Pedrycz W, Li Z. Development and Analysis of Neural Networks Realized in the Presence of Granular Data. Ieee Transactions On Neural Networks and Learning Systems. PMID 31722490 DOI: 10.1109/Tnnls.2019.2945307 |
0.369 |
|
2019 |
Mencar C, Pedrycz W. GrCount: Counting method for uncertain data. Methodsx. 6: 2455-2459. PMID 31720235 DOI: 10.1016/J.Mex.2019.10.001 |
0.429 |
|
2019 |
Feng G, Lu W, Pedrycz W, Yang J, Liu X. The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy. Ieee Transactions On Cybernetics. PMID 31443065 DOI: 10.1109/Tcyb.2019.2933438 |
0.326 |
|
2019 |
Lu W, Pedrycz W, Yang J, Liu X. Granular Fuzzy Modeling Guided Through the Synergy of Granulating Output Space and Clustering Input Subspaces. Ieee Transactions On Cybernetics. PMID 31021786 DOI: 10.1109/Tcyb.2019.2909037 |
0.465 |
|
2019 |
Wu Y, Dong Y, Qin J, Pedrycz W. Flexible Linguistic Expressions and Consensus Reaching With Accurate Constraints in Group Decision-Making. Ieee Transactions On Cybernetics. PMID 30990204 DOI: 10.1109/Tcyb.2019.2906318 |
0.346 |
|
2019 |
Zhao J, Wang T, Pedrycz W, Wang W. Granular Prediction and Dynamic Scheduling Based on Adaptive Dynamic Programming for the Blast Furnace Gas System. Ieee Transactions On Cybernetics. PMID 30951483 DOI: 10.1109/Tcyb.2019.2901268 |
0.338 |
|
2019 |
Ouyang T, Pedrycz W, Pizzi NJ. Rule-Based Modeling With DBSCAN-Based Information Granules. Ieee Transactions On Cybernetics. PMID 30908270 DOI: 10.1109/Tcyb.2019.2902603 |
0.428 |
|
2019 |
Zhu X, Pedrycz W, Li Z. A Development of Granular Input Space in System Modeling. Ieee Transactions On Cybernetics. PMID 30892261 DOI: 10.1109/Tcyb.2019.2899633 |
0.4 |
|
2019 |
Ouyang T, Pedrycz W, Reyes-Galaviz OF, Pizzi NJ. Granular Description of Data Structures: A Two-Phase Design. Ieee Transactions On Cybernetics. PMID 30605118 DOI: 10.1109/Tcyb.2018.2887115 |
0.4 |
|
2019 |
Shen Y, Pedrycz W, Wang X. Approximation of Fuzzy Sets by Interval Type-2 Trapezoidal Fuzzy Sets. Ieee Transactions On Cybernetics. PMID 30605116 DOI: 10.1109/Tcyb.2018.2886725 |
0.493 |
|
2019 |
Karczmarek Pl, Pedrycz W, Kiersztyn A, Dolecki Ml. A comprehensive experimental comparison of the aggregation techniques for face recognition Iranian Journal of Fuzzy Systems. 16: 1-19. DOI: 10.22111/Ijfs.2019.4778 |
0.312 |
|
2019 |
Kozik R, Pawlicki M, Choraś M, Pedrycz W. Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection Complexity. 2019: 1-9. DOI: 10.1155/2019/5826737 |
0.352 |
|
2019 |
Chen H, Cheng R, Pedrycz W, Jin Y. Solving Many-Objective Optimization Problems via Multistage Evolutionary Search Ieee Transactions On Systems, Man, and Cybernetics. 1-13. DOI: 10.1109/Tsmc.2019.2930737 |
0.326 |
|
2019 |
Ng WWY, Zhang J, Lai CS, Pedrycz W, Lai LL, Wang X. Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification Ieee Transactions On Industrial Informatics. 15: 1588-1597. DOI: 10.1109/Tii.2018.2850930 |
0.332 |
|
2019 |
Xu K, Pedrycz W, Li Z, Nie W. High-Accuracy Signal Subspace Separation Algorithm Based on Gaussian Kernel Soft Partition Ieee Transactions On Industrial Electronics. 66: 491-499. DOI: 10.1109/Tie.2018.2823666 |
0.308 |
|
2019 |
Zhao J, Chen L, Pedrycz W, Wang W. Variational Inference-Based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data Ieee Transactions On Industrial Electronics. 66: 416-428. DOI: 10.1109/Tie.2018.2815997 |
0.35 |
|
2019 |
Roh S, Oh S, Pedrycz W, Fu Z. Design of Fuzzy Ensemble Architecture Realized with the Aid of FCM-based Fuzzy Partition and NN with Weighted LSE Estimation Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2019.2956903 |
0.386 |
|
2019 |
Shen Y, Pedrycz W, Chen Y, Wang X, Gacek A. Hyperplane Division in Fuzzy C-Means: Clustering Big Data Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2019.2947231 |
0.397 |
|
2019 |
Lu W, Feng G, Liu X, Pedrycz W, Zhang L, Yang J. Fast and Effective Learning for Fuzzy Cognitive Maps: A Method Based on Solving Constrained Convex Optimization Problems Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2019.2946119 |
0.35 |
|
2019 |
Liu X, Jia W, Liu W, Pedrycz W. AFSSE: An Interpretable Classifier with Axiomatic Fuzzy Set and Semantic Entropy Ieee Transactions On Fuzzy Systems. 1-1. DOI: 10.1109/Tfuzz.2019.2945239 |
0.445 |
|
2019 |
Xu K, Pedrycz W, Li Z, Nie W. Constructing a Virtual Space for Enhancing the Classification Performance of Fuzzy Clustering Ieee Transactions On Fuzzy Systems. 27: 1779-1792. DOI: 10.1109/Tfuzz.2018.2889020 |
0.436 |
|
2019 |
Lu W, Shan D, Pedrycz W, Zhang L, Yang J, Liu X. Granular Fuzzy Modeling for Multidimensional Numeric Data: A Layered Approach Based on Hyperbox Ieee Transactions On Fuzzy Systems. 27: 775-789. DOI: 10.1109/Tfuzz.2018.2870050 |
0.488 |
|
2019 |
Zuo H, Lu J, Zhang G, Pedrycz W. Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces Ieee Transactions On Fuzzy Systems. 27: 348-361. DOI: 10.1109/Tfuzz.2018.2853720 |
0.345 |
|
2019 |
Wang D, Pedrycz W, Li Z. A Two-Phase Development of Fuzzy Rule-Based Model and Their Analysis Ieee Access. 7: 80328-80341. DOI: 10.1109/Access.2019.2919739 |
0.412 |
|
2019 |
Wang H, Xu C, Xu Z, Zeng X, Pedrycz W. An Aspiration-Based Approach for Qualitative Decision-Making With Complex Linguistic Expressions Ieee Access. 7: 12529-12546. DOI: 10.1109/Access.2019.2892844 |
0.397 |
|
2019 |
Zhang H, Zhang T, Pedrycz W, Zhao C, Miao D. Improved adaptive image retrieval with the use of shadowed sets Pattern Recognition. 90: 390-403. DOI: 10.1016/J.Patcog.2019.01.029 |
0.307 |
|
2019 |
Tang Y, Hu X, Pedrycz W, Song X. Possibilistic fuzzy clustering with high-density viewpoint Neurocomputing. 329: 407-423. DOI: 10.1016/J.Neucom.2018.11.007 |
0.415 |
|
2019 |
Vargas JA, Pedrycz W, Hemerly EM. Improved learning algorithm for two-layer neural networks for identification of nonlinear systems Neurocomputing. 329: 86-96. DOI: 10.1016/J.Neucom.2018.10.008 |
0.329 |
|
2019 |
Al-Hmouz R, Pedrycz W, Balamash A, Morfeq A. Logic-driven autoencoders Knowledge-Based Systems. 183: 104874. DOI: 10.1016/J.Knosys.2019.104874 |
0.437 |
|
2019 |
Hu X, Pedrycz W, Wang X. Random ensemble of fuzzy rule-based models Knowledge Based Systems. 181: 104768. DOI: 10.1016/J.Knosys.2019.05.011 |
0.486 |
|
2019 |
Fu C, Lu W, Pedrycz W, Yang J. Fuzzy granular classification based on the principle of justifiable granularity Knowledge-Based Systems. 170: 89-101. DOI: 10.1016/J.Knosys.2019.02.001 |
0.487 |
|
2019 |
E H, Cui Y, Pedrycz W, Li Z. Enhancements of rule-based models through refinements of Fuzzy C-Means Knowledge-Based Systems. 170: 43-60. DOI: 10.1016/J.Knosys.2019.01.027 |
0.506 |
|
2019 |
Ju H, Pedrycz W, Li H, Ding W, Yang X, Zhou X. Sequential three-way classifier with justifiable granularity Knowledge-Based Systems. 163: 103-119. DOI: 10.1016/J.Knosys.2018.08.022 |
0.425 |
|
2019 |
Ramalho FD, Ekel PY, Pedrycz W, Pereira Júnior JG, Soares GL. Multicriteria decision making under conditions of uncertainty in application to multiobjective allocation of resources Information Fusion. 49: 249-261. DOI: 10.1016/J.Inffus.2018.12.010 |
0.414 |
|
2019 |
Jing X, Yan Z, Jiang X, Pedrycz W. Network traffic fusion and analysis against DDoS flooding attacks with a novel reversible sketch Information Fusion. 51: 100-113. DOI: 10.1016/J.Inffus.2018.10.013 |
0.304 |
|
2019 |
Roh S, Oh S, Pedrycz W, Seo K, Fu Z. Design methodology for Radial Basis Function Neural Networks classifier based on locally linear reconstruction and Conditional Fuzzy C-Means clustering International Journal of Approximate Reasoning. 106: 228-243. DOI: 10.1016/J.Ijar.2019.01.008 |
0.484 |
|
2019 |
Hu X, Pedrycz W, Wang D. Fuzzy rule-based models with randomized development mechanisms Fuzzy Sets and Systems. 361: 71-87. DOI: 10.1016/J.Fss.2018.09.001 |
0.477 |
|
2019 |
Yao N, Miao D, Pedrycz W, Zhang H, Zhang Z. Causality measures and analysis: A rough set framework Expert Systems With Applications. 136: 187-200. DOI: 10.1016/J.Eswa.2019.06.004 |
0.369 |
|
2019 |
Mirończuk MM, Protasiewicz J, Pedrycz W. Empirical evaluation of feature projection algorithms for multi-view text classification Expert Systems With Applications. 130: 97-112. DOI: 10.1016/J.Eswa.2019.04.020 |
0.381 |
|
2019 |
Wang X, Yu F, Pedrycz W, Yu L. Clustering of interval-valued time series of unequal length based on improved dynamic time warping Expert Systems With Applications. 125: 293-304. DOI: 10.1016/J.Eswa.2019.01.005 |
0.354 |
|
2019 |
Ouyang T, Pedrycz W, Pizzi NJ. Record linkage based on a three-way decision with the use of granular descriptors Expert Systems With Applications. 122: 16-26. DOI: 10.1016/J.Eswa.2018.12.038 |
0.389 |
|
2019 |
Hu J, Wu M, Chen X, Cao W, Pedrycz W. Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process Control Engineering Practice. 88: 141-151. DOI: 10.1016/J.Conengprac.2019.05.009 |
0.33 |
|
2019 |
Wang L, Wang Y, Pedrycz W. Hesitant 2-tuple linguistic Bonferroni operators and their utilization in group decision making Applied Soft Computing. 77: 653-664. DOI: 10.1016/J.Asoc.2019.01.038 |
0.36 |
|
2019 |
Song M, Jing Y, Pedrycz W. Granular neural networks: A study of optimizing allocation of information granularity in input space Applied Soft Computing. 77: 67-75. DOI: 10.1016/J.Asoc.2019.01.013 |
0.617 |
|
2019 |
Soto J, Castillo O, Melin P, Pedrycz W. A New Approach to Multiple Time Series Prediction Using MIMO Fuzzy Aggregation Models with Modular Neural Networks International Journal of Fuzzy Systems. 21: 1629-1648. DOI: 10.1007/S40815-019-00642-W |
0.474 |
|
2019 |
Tang X, Zhang Q, Peng Z, Yang S, Pedrycz W. Derivation of personalized numerical scales from distribution linguistic preference relations: an expected consistency-based goal programming approach Neural Computing and Applications. 31: 8769-8786. DOI: 10.1007/S00521-019-04466-5 |
0.359 |
|
2019 |
Zhou J, Gao C, Pedrycz W, Lai Z, Yue X. Constrained shadowed sets and fast optimization algorithm International Journal of Intelligent Systems. 34: 2655-2675. DOI: 10.1002/Int.22170 |
0.321 |
|
2018 |
Pratama M, Dimla E, Tjahjowidodo T, Pedrycz W, Lughofer E. Online Tool Condition Monitoring Based on Parsimonious Ensemble. Ieee Transactions On Cybernetics. PMID 30334774 DOI: 10.1109/Tcyb.2018.2871120 |
0.382 |
|
2018 |
Ding W, Lin CT, Pedrycz W. Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce. Ieee Transactions On Cybernetics. PMID 30130243 DOI: 10.1109/Tcyb.2018.2859342 |
0.344 |
|
2018 |
Liu F, Pedrycz W, Liu XW. Flexibility Degree of Fuzzy Numbers and Its Implication to a Group-Decision-Making Model. Ieee Transactions On Cybernetics. PMID 30059328 DOI: 10.1109/Tcyb.2018.2853722 |
0.48 |
|
2018 |
Cimino MGCA, Lazzeri A, Pedrycz W, Vaglini G. Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions. Sensors (Basel, Switzerland). 18. PMID 30004417 DOI: 10.3390/S18072117 |
0.332 |
|
2018 |
Ng WWY, Tian X, Pedrycz W, Wang X, Yeung DS. Incremental Hash-Bit Learning for Semantic Image Retrieval in Nonstationary Environments. Ieee Transactions On Cybernetics. PMID 29994699 DOI: 10.1109/Tcyb.2018.2846760 |
0.352 |
|
2018 |
Shen Y, Pedrycz W, Wang X. Clustering Homogeneous Granular Data: Formation and Evaluation. Ieee Transactions On Cybernetics. PMID 29994448 DOI: 10.1109/Tcyb.2018.2802453 |
0.391 |
|
2018 |
Rong M, Gong D, Zhang Y, Jin Y, Pedrycz W. Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems. Ieee Transactions On Cybernetics. PMID 29994141 DOI: 10.1109/Tcyb.2018.2842158 |
0.336 |
|
2018 |
Zhang Z, Pedrycz W. A Consistency and Consensus-Based Goal Programming Method for Group Decision-Making With Interval-Valued Intuitionistic Multiplicative Preference Relations. Ieee Transactions On Cybernetics. PMID 29994140 DOI: 10.1109/Tcyb.2018.2842073 |
0.387 |
|
2018 |
Nguyen TT, Pham XC, Liew AW, Pedrycz W. Aggregation of Classifiers: A Justifiable Information Granularity Approach. Ieee Transactions On Cybernetics. PMID 29993920 DOI: 10.1109/Tcyb.2018.2821679 |
0.429 |
|
2018 |
Kim EH, Oh SK, Pedrycz W. Design of double fuzzy clustering-driven context neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 104: 1-14. PMID 29689457 DOI: 10.1016/J.Neunet.2018.03.018 |
0.379 |
|
2018 |
Liu C, Pedrycz W, Qian J, Wang M. Covering-based multigranulation decision-theoretic rough set approaches with new strategies Journal of Intelligent & Fuzzy Systems. 35: 1179-1191. DOI: 10.3233/Jifs-18233 |
0.347 |
|
2018 |
Liu C, Pedrycz W, Jiang F, Wang M. Decision-theoretic rough set approaches to multi-covering approximation spaces based on fuzzy probability measure Journal of Intelligent & Fuzzy Systems. 34: 1917-1931. DOI: 10.3233/Jifs-171275 |
0.438 |
|
2018 |
Zhao J, Chen L, Pedrycz W, Wang W. A Novel Semi-Supervised Sparse Bayesian Regression Based on Variational Inference for Industrial Datasets With Incomplete Outputs Ieee Transactions On Systems, Man, and Cybernetics. 1-14. DOI: 10.1109/Tsmc.2018.2864752 |
0.301 |
|
2018 |
Roh S, Oh S, Pedrycz W. Identification of Black Plastics Based on Fuzzy RBF Neural Networks: Focused on Data Preprocessing Techniques Through Fourier Transform Infrared Radiation Ieee Transactions On Industrial Informatics. 14: 1802-1813. DOI: 10.1109/Tii.2017.2771254 |
0.356 |
|
2018 |
Zhu X, Pedrycz W, Li Z. Granular Models and Granular Outliers Ieee Transactions On Fuzzy Systems. 26: 3835-3846. DOI: 10.1109/Tfuzz.2018.2849736 |
0.423 |
|
2018 |
Zhang Z, Pedrycz W. Goal Programming Approaches to Managing Consistency and Consensus for Intuitionistic Multiplicative Preference Relations in Group Decision Making Ieee Transactions On Fuzzy Systems. 26: 3261-3275. DOI: 10.1109/Tfuzz.2018.2818074 |
0.334 |
|
2018 |
Zhu X, Pedrycz W, Li Z. A Design of Granular Takagi–Sugeno Fuzzy Model Through the Synergy of Fuzzy Subspace Clustering and Optimal Allocation of Information Granularity Ieee Transactions On Fuzzy Systems. 26: 2499-2509. DOI: 10.1109/Tfuzz.2018.2813314 |
0.503 |
|
2018 |
Guo H, Pedrycz W, Liu X. Hidden Markov Models Based Approaches to Long-Term Prediction for Granular Time Series Ieee Transactions On Fuzzy Systems. 26: 2807-2817. DOI: 10.1109/Tfuzz.2018.2802924 |
0.326 |
|
2018 |
Pratama M, Pedrycz W, Lughofer E. Evolving Ensemble Fuzzy Classifier Ieee Transactions On Fuzzy Systems. 26: 2552-2567. DOI: 10.1109/Tfuzz.2018.2796099 |
0.356 |
|
2018 |
Liu F, Wu Y, Pedrycz W. A Modified Consensus Model in Group Decision Making With an Allocation of Information Granularity Ieee Transactions On Fuzzy Systems. 26: 3182-3187. DOI: 10.1109/Tfuzz.2018.2793885 |
0.384 |
|
2018 |
Duan X, Wang Y, Pedrycz W, Liu X, Wang C, Li Z. AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks Ieee Transactions On Fuzzy Systems. 26: 3151-3163. DOI: 10.1109/Tfuzz.2017.2788875 |
0.444 |
|
2018 |
Kim E, Oh S, Pedrycz W. Design of Reinforced Interval Type-2 Fuzzy C-Means-Based Fuzzy Classifier Ieee Transactions On Fuzzy Systems. 26: 3054-3068. DOI: 10.1109/Tfuzz.2017.2785244 |
0.456 |
|
2018 |
Zhu X, Pedrycz W, Li Z. Granular Representation of Data: A Design of Families of ϵ-Information Granules Ieee Transactions On Fuzzy Systems. 26: 2107-2119. DOI: 10.1109/Tfuzz.2017.2763122 |
0.443 |
|
2018 |
Mehlawat MK, Gupta P, Pedrycz W. A New Possibilistic Optimization Model for Multiple Criteria Assignment Problem Ieee Transactions On Fuzzy Systems. 26: 1775-1788. DOI: 10.1109/Tfuzz.2017.2751006 |
0.389 |
|
2018 |
Zuo H, Zhang G, Pedrycz W, Behbood V, Lu J. Granular Fuzzy Regression Domain Adaptation in Takagi–Sugeno Fuzzy Models Ieee Transactions On Fuzzy Systems. 26: 847-858. DOI: 10.1109/Tfuzz.2017.2694801 |
0.408 |
|
2018 |
Al-Hmouz R, Pedrycz W, Balamash AS, Morfeq A. Hierarchical System Modeling Ieee Transactions On Fuzzy Systems. 26: 258-269. DOI: 10.1109/Tfuzz.2017.2649581 |
0.435 |
|
2018 |
Hu Q, Zhang L, Zhou Y, Pedrycz W. Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets Ieee Transactions On Fuzzy Systems. 26: 226-238. DOI: 10.1109/Tfuzz.2017.2647966 |
0.515 |
|
2018 |
Segatori A, Marcelloni F, Pedrycz W. On Distributed Fuzzy Decision Trees for Big Data Ieee Transactions On Fuzzy Systems. 26: 174-192. DOI: 10.1109/Tfuzz.2016.2646746 |
0.479 |
|
2018 |
Shoniker M, Oleynikov O, Cockburn BF, Han J, Rana M, Pedrycz W. Automatic Selection of Process Corner Simulations for Faster Design Verification Ieee Transactions On Computer-Aided Design of Integrated Circuits and Systems. 37: 1312-1316. DOI: 10.1109/Tcad.2017.2748027 |
0.301 |
|
2018 |
Pedrycz W. Granular computing for data analytics: a manifesto of human-centric computing Ieee/Caa Journal of Automatica Sinica. 5: 1025-1034. DOI: 10.1109/Jas.2018.7511213 |
0.334 |
|
2018 |
Ren H, Li X, Li Z, Pedrycz W. Data Representation Based on Interval-Sets for Anomaly Detection in Time Series Ieee Access. 6: 27473-27479. DOI: 10.1109/Access.2018.2828864 |
0.413 |
|
2018 |
Li W, Pedrycz W, Xue X, Zhang X, Fan B, Long B. Information measure of absolute and relative quantification in double-quantitative decision-theoretic rough set model The Journal of Engineering. 2018: 1436-1441. DOI: 10.1049/Joe.2018.8315 |
0.354 |
|
2018 |
Sheri AM, Rafique MA, Jeon M, Pedrycz W. Background subtraction using Gaussian–Bernoulli restricted Boltzmann machine Iet Image Processing. 12: 1646-1654. DOI: 10.1049/Iet-Ipr.2017.1055 |
0.33 |
|
2018 |
Ngo LT, Dang TH, Pedrycz W. Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms Pattern Recognition. 81: 404-416. DOI: 10.1016/J.Patcog.2018.04.006 |
0.45 |
|
2018 |
Hu X, Pedrycz W, Wang X. Fuzzy classifiers with information granules in feature space and logic-based computing Pattern Recognition. 80: 156-167. DOI: 10.1016/J.Patcog.2018.03.011 |
0.5 |
|
2018 |
Loia V, Parente D, Pedrycz W, Tomasiello S. A Granular Functional Network with delay: Some dynamical properties and application to the sign prediction in social networks Neurocomputing. 321: 61-71. DOI: 10.1016/J.Neucom.2018.08.047 |
0.373 |
|
2018 |
Kim E, Oh S, Pedrycz W. Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis Neurocomputing. 303: 20-36. DOI: 10.1016/J.Neucom.2018.04.003 |
0.492 |
|
2018 |
Zheng Y, He Y, Xu Z, Pedrycz W. Assessment for hierarchical medical policy proposals using hesitant fuzzy linguistic analytic network process Knowledge-Based Systems. 161: 254-267. DOI: 10.1016/J.Knosys.2018.07.005 |
0.44 |
|
2018 |
Loia V, Orciuoli F, Pedrycz W. Towards a granular computing approach based on Formal Concept Analysis for discovering periodicities in data Knowledge-Based Systems. 146: 1-11. DOI: 10.1016/J.Knosys.2018.01.032 |
0.384 |
|
2018 |
Liu F, Yu Q, Pedrycz W, Zhang W. A group decision making model based on an inconsistency index of interval multiplicative reciprocal matrices Knowledge-Based Systems. 145: 67-76. DOI: 10.1016/J.Knosys.2018.01.001 |
0.388 |
|
2018 |
Zhou X, Xu Z, Yao L, Tu Y, Lev B, Pedrycz W. A novel Data Envelopment Analysis model for evaluating industrial production and environmental management system Journal of Cleaner Production. 170: 773-788. DOI: 10.1016/J.Jclepro.2017.09.160 |
0.416 |
|
2018 |
Aliev R, Pedrycz W, Huseynov O. Hukuhara difference of Z-numbers Information Sciences. 466: 13-24. DOI: 10.1016/J.Ins.2018.07.033 |
0.444 |
|
2018 |
Pedrycz W, Krawczak M, Zadrozny S. Computational intelligence techniques for decision support, data mining and information searching Information Sciences. 374-376. DOI: 10.1016/J.Ins.2018.06.053 |
0.328 |
|
2018 |
Aliev R, Pedrycz W, Huseynov O. Functions defined on a set of Z-numbers Information Sciences. 423: 353-375. DOI: 10.1016/J.Ins.2017.09.056 |
0.426 |
|
2018 |
Li W, Pedrycz W, Xue X, Xu W, Fan B. Distance-based double-quantitative rough fuzzy sets with logic operations International Journal of Approximate Reasoning. 101: 206-233. DOI: 10.1016/J.Ijar.2018.07.007 |
0.504 |
|
2018 |
Tang Y, Pedrycz W. On the α ( u , v )-symmetric implicational method for R- and (S, N)-implications International Journal of Approximate Reasoning. 92: 212-231. DOI: 10.1016/J.Ijar.2017.10.009 |
0.342 |
|
2018 |
Su Y, Liu H, Pedrycz W. A method to construct fuzzy implications–rotation construction International Journal of Approximate Reasoning. 92: 20-31. DOI: 10.1016/J.Ijar.2017.10.003 |
0.455 |
|
2018 |
Di Martino F, Pedrycz W, Sessa S. Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction Fuzzy Sets and Systems. 340: 109-126. DOI: 10.1016/J.Fss.2017.11.011 |
0.399 |
|
2018 |
Cabrerizo FJ, Morente-Molinera JA, Pedrycz W, Taghavi A, Herrera-Viedma E. Granulating linguistic information in decision making under consensus and consistency Expert Systems With Applications. 99: 83-92. DOI: 10.1016/J.Eswa.2018.01.030 |
0.391 |
|
2018 |
Liu S, Pedrycz W, Gacek A, Dai Y. Development of information granules of higher type and their applications to granular models of time series Engineering Applications of Artificial Intelligence. 71: 60-72. DOI: 10.1016/J.Engappai.2018.02.012 |
0.389 |
|
2018 |
Duan L, Yu F, Pedrycz W, Wang X, Yang X. Time-series clustering based on linear fuzzy information granules Applied Soft Computing. 73: 1053-1067. DOI: 10.1016/J.Asoc.2018.09.032 |
0.41 |
|
2018 |
Nguyen TT, Nguyen MP, Pham XC, Liew AW, Pedrycz W. Combining heterogeneous classifiers via granular prototypes Applied Soft Computing. 73: 795-815. DOI: 10.1016/J.Asoc.2018.09.021 |
0.378 |
|
2018 |
Gupta P, Mehlawat MK, Grover N, Pedrycz W. Multi-attribute group decision making based on extended TOPSIS method under interval-valued intuitionistic fuzzy environment Applied Soft Computing. 69: 554-567. DOI: 10.1016/J.Asoc.2018.04.032 |
0.409 |
|
2018 |
Tang X, Yang S, Pedrycz W. Multiple attribute decision-making approach based on dual hesitant fuzzy Frank aggregation operators Applied Soft Computing. 68: 525-547. DOI: 10.1016/J.Asoc.2018.03.055 |
0.426 |
|
2018 |
Zhou H, Song M, Pedrycz W. A comparative study of improved GA and PSO in solving multiple traveling salesmen problem Applied Soft Computing. 64: 564-580. DOI: 10.1016/J.Asoc.2017.12.031 |
0.551 |
|
2018 |
Zhang Z, Pedrycz W, Huang J. Efficient mining product-based fuzzy association rules through central limit theorem Applied Soft Computing. 63: 235-248. DOI: 10.1016/J.Asoc.2017.11.025 |
0.443 |
|
2018 |
Kolasa M, Długosz R, Talaśka T, Pedrycz W. Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware Applied Mathematics and Computation. 319: 31-47. DOI: 10.1016/J.Amc.2017.01.043 |
0.315 |
|
2018 |
Karczmarek P, Kiersztyn A, Pedrycz W, Dolecki M. Linguistic Descriptors in Face Recognition International Journal of Fuzzy Systems. 20: 2668-2676. DOI: 10.1007/S40815-018-0517-0 |
0.321 |
|
2018 |
Li W, Pedrycz W, Xue X, Xu W, Fan B. Fuzziness and incremental information of disjoint regions in double-quantitative decision-theoretic rough set model International Journal of Machine Learning and Cybernetics. 10: 2669-2690. DOI: 10.1007/S13042-018-0893-7 |
0.433 |
|
2018 |
Bae J, Oh S, Pedrycz W, Fu Z. Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy Applied Intelligence. 49: 929-949. DOI: 10.1007/S10489-018-1300-5 |
0.368 |
|
2018 |
Guo H, Pedrycz W, Liu X. Fuzzy time series forecasting based on axiomatic fuzzy set theory Neural Computing and Applications. 31: 3921-3932. DOI: 10.1007/S00521-017-3325-9 |
0.497 |
|
2017 |
Xu J, Wang G, Li T, Pedrycz W. Local-Density-Based Optimal Granulation and Manifold Information Granule Description. Ieee Transactions On Cybernetics. PMID 28945607 DOI: 10.1109/Tcyb.2017.2750481 |
0.356 |
|
2017 |
Huang W, Oh SK, Pedrycz W. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons. Ieee Transactions On Neural Networks and Learning Systems. PMID 28809719 DOI: 10.1109/Tnnls.2017.2729589 |
0.465 |
|
2017 |
Zhang Z, Pedrycz W. Intuitionistic Multiplicative Group Analytic Hierarchy Process and Its Use in Multicriteria Group Decision-Making. Ieee Transactions On Cybernetics. PMID 28727567 DOI: 10.1109/Tcyb.2017.2720167 |
0.39 |
|
2017 |
Zhang X, Zhuang Y, Wang W, Pedrycz W. Online Feature Transformation Learning for Cross-Domain Object Category Recognition. Ieee Transactions On Neural Networks and Learning Systems. PMID 28613184 DOI: 10.1109/Tnnls.2017.2705113 |
0.321 |
|
2017 |
Liu C, Pedrycz W, Wang M. Covering-based multigranulation decision-theoretic rough sets Journal of Intelligent & Fuzzy Systems. 32: 749-765. DOI: 10.3233/Jifs-16020 |
0.351 |
|
2017 |
Liu F, Peng Y, Zhang W, Pedrycz W. On Consistency in AHP and Fuzzy AHP Journal of Systems Science and Information. 5: 128-147. DOI: 10.21078/Jssi-2017-128-20 |
0.463 |
|
2017 |
Liang D, Pedrycz W, Liu D. Determining Three-Way Decisions With Decision-Theoretic Rough Sets Using a Relative Value Approach Ieee Transactions On Systems, Man, and Cybernetics: Systems. 47: 1785-1799. DOI: 10.1109/Tsmc.2016.2531644 |
0.356 |
|
2017 |
Zuo H, Zhang G, Pedrycz W, Behbood V, Lu J. Fuzzy Regression Transfer Learning in Takagi–Sugeno Fuzzy Models Ieee Transactions On Fuzzy Systems. 25: 1795-1807. DOI: 10.1109/Tfuzz.2016.2633376 |
0.384 |
|
2017 |
Zhu X, Pedrycz W, Li Z. Granular Encoders and Decoders: A Study in Processing Information Granules Ieee Transactions On Fuzzy Systems. 25: 1115-1126. DOI: 10.1109/Tfuzz.2016.2598366 |
0.454 |
|
2017 |
Zhang Z, Pedrycz W. Models of Mathematical Programming for Intuitionistic Multiplicative Preference Relations Ieee Transactions On Fuzzy Systems. 25: 945-957. DOI: 10.1109/Tfuzz.2016.2587326 |
0.427 |
|
2017 |
Zhao J, Sheng C, Wang W, Pedrycz W, Liu Q. Data-Based Predictive Optimization for Byproduct Gas System in Steel Industry Ieee Transactions On Automation Science and Engineering. 14: 1761-1770. DOI: 10.1109/Tase.2016.2629505 |
0.359 |
|
2017 |
Abdolrazzaghi M, Zarifi MH, Pedrycz W, Daneshmand M. Robust Ultra-High Resolution Microwave Planar Sensor Using Fuzzy Neural Network Approach Ieee Sensors Journal. 17: 323-332. DOI: 10.1109/Jsen.2016.2631618 |
0.369 |
|
2017 |
Liu S, Pedrycz W, Gacek A, Dai Y. A two-phase method of forming a granular representation of signals Signal Processing. 141: 1-15. DOI: 10.1016/J.Sigpro.2017.05.026 |
0.377 |
|
2017 |
Pedrycz W. Selected insights into building data associations and their granular augmentations Procedia Computer Science. 120: 4. DOI: 10.1016/J.Procs.2017.11.200 |
0.331 |
|
2017 |
Li F, Miao D, Pedrycz W. Granular multi-label feature selection based on mutual information Pattern Recognition. 67: 410-423. DOI: 10.1016/J.Patcog.2017.02.025 |
0.328 |
|
2017 |
Li T, Zhang L, Lu W, Hou H, Liu X, Pedrycz W, Zhong C. Interval kernel Fuzzy C-Means clustering of incomplete data Neurocomputing. 237: 316-331. DOI: 10.1016/J.Neucom.2017.01.017 |
0.397 |
|
2017 |
Hu X, Pedrycz W, Wang X. Development of granular models through the design of a granular output spaces Knowledge-Based Systems. 134: 159-171. DOI: 10.1016/J.Knosys.2017.07.030 |
0.375 |
|
2017 |
Ren H, Liu M, Li Z, Pedrycz W. A Piecewise Aggregate pattern representation approach for anomaly detection in time series Knowledge-Based Systems. 135: 29-39. DOI: 10.1016/J.Knosys.2017.07.021 |
0.353 |
|
2017 |
Truong HQ, Ngo LT, Pedrycz W. Granular Fuzzy Possibilistic C-Means Clustering approach to DNA microarray problem Knowledge-Based Systems. 133: 53-65. DOI: 10.1016/J.Knosys.2017.06.019 |
0.405 |
|
2017 |
Hu X, Pedrycz W, Wang X. From fuzzy rule-based models to their granular generalizations Knowledge-Based Systems. 124: 133-143. DOI: 10.1016/J.Knosys.2017.03.007 |
0.505 |
|
2017 |
Kim E, Oh S, Pedrycz W. Reinforced rule-based fuzzy models: Design and analysis Knowledge-Based Systems. 119: 44-58. DOI: 10.1016/J.Knosys.2016.12.003 |
0.498 |
|
2017 |
Wang H, Xu Z, Pedrycz W. An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities Knowledge-Based Systems. 118: 15-30. DOI: 10.1016/J.Knosys.2016.11.008 |
0.477 |
|
2017 |
Froelich W, Pedrycz W. Fuzzy cognitive maps in the modeling of granular time series Knowledge-Based Systems. 115: 110-122. DOI: 10.1016/J.Knosys.2016.10.017 |
0.468 |
|
2017 |
Qian Y, Cheng H, Wang J, Liang J, Pedrycz W, Dang C. Grouping granular structures in human granulation intelligence Information Sciences. 382: 150-169. DOI: 10.1016/J.Ins.2016.11.024 |
0.328 |
|
2017 |
Yan Z, Jing X, Pedrycz W. Fusing and mining opinions for reputation generation Information Fusion. 36: 172-184. DOI: 10.1016/J.Inffus.2016.11.011 |
0.339 |
|
2017 |
Zhang L, Zhong W, Zhong C, Lu W, Liu X, Pedrycz W. Fuzzy C-Means clustering based on dual expression between cluster prototypes and reconstructed data International Journal of Approximate Reasoning. 90: 389-410. DOI: 10.1016/J.Ijar.2017.08.008 |
0.375 |
|
2017 |
Su Y, Liu H, Pedrycz W. Coimplications derived from pseudo-uninorms on a complete lattice International Journal of Approximate Reasoning. 90: 107-119. DOI: 10.1016/J.Ijar.2017.07.006 |
0.396 |
|
2017 |
Shen Y, Pedrycz W. Collaborative fuzzy clustering algorithm: Some refinements International Journal of Approximate Reasoning. 86: 41-61. DOI: 10.1016/J.Ijar.2017.04.004 |
0.426 |
|
2017 |
Balamash A, Pedrycz W, Al-Hmouz R, Morfeq A. Perspective-oriented data analysis through the development of information granules of order 2 International Journal of Approximate Reasoning. 85: 97-106. DOI: 10.1016/J.Ijar.2017.03.006 |
0.426 |
|
2017 |
Yang X, Yu F, Pedrycz W. Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system International Journal of Approximate Reasoning. 81: 1-27. DOI: 10.1016/J.Ijar.2016.10.010 |
0.466 |
|
2017 |
Liu F, Pedrycz W, Wang Z, Zhang W. An axiomatic approach to approximation-consistency of triangular fuzzy reciprocal preference relations Fuzzy Sets and Systems. 322: 1-18. DOI: 10.1016/J.Fss.2017.02.004 |
0.413 |
|
2017 |
Reyes-Galaviz OF, Pedrycz W. Enhancement of the classification and reconstruction performance of fuzzy C-means with refinements of prototypes Fuzzy Sets and Systems. 318: 80-99. DOI: 10.1016/J.Fss.2016.07.002 |
0.461 |
|
2017 |
Zhou X, Yu N, Tu Y, Pedrycz W, Lev B. Bi-level plant selection and production allocation model under type-2 fuzzy demand Expert Systems With Applications. 86: 87-98. DOI: 10.1016/J.Eswa.2017.05.057 |
0.427 |
|
2017 |
Vo B, Le T, Pedrycz W, Nguyen G, Baik SW. Mining erasable itemsets with subset and superset itemset constraints Expert Systems With Applications. 69: 50-61. DOI: 10.1016/J.Eswa.2016.10.028 |
0.329 |
|
2017 |
Zhang Z, Pedrycz W, Huang J. Efficient frequent itemsets mining through sampling and information granulation Engineering Applications of Artificial Intelligence. 65: 119-136. DOI: 10.1016/J.Engappai.2017.07.016 |
0.342 |
|
2017 |
Qin J, Liu X, Pedrycz W. An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment European Journal of Operational Research. 258: 626-638. DOI: 10.1016/J.Ejor.2016.09.059 |
0.429 |
|
2017 |
Świetlicka A, Gugała K, Pedrycz W, Rybarczyk A. Development of the deterministic and stochastic Markovian model of a dendritic neuron Biocybernetics and Biomedical Engineering. 37: 201-216. DOI: 10.1016/J.Bbe.2016.10.002 |
0.31 |
|
2017 |
Wu G, Pedrycz W, Suganthan P, Li H. Using variable reduction strategy to accelerate evolutionary optimization Applied Soft Computing. 61: 283-293. DOI: 10.1016/J.Asoc.2017.08.012 |
0.304 |
|
2017 |
Zhu X, Pedrycz W, Li Z. Fuzzy clustering with nonlinearly transformed data Applied Soft Computing. 61: 364-376. DOI: 10.1016/J.Asoc.2017.07.026 |
0.42 |
|
2017 |
Li J, Pedrycz W, Jamal I. Multivariate time series anomaly detection: A framework of Hidden Markov Models Applied Soft Computing. 60: 229-240. DOI: 10.1016/J.Asoc.2017.06.035 |
0.376 |
|
2017 |
Machado-Coelho T, Machado A, Jaulin L, Ekel P, Pedrycz W, Soares G. An interval space reducing method for constrained problems with particle swarm optimization Applied Soft Computing. 59: 405-417. DOI: 10.1016/J.Asoc.2017.05.022 |
0.346 |
|
2017 |
Hu X, Pedrycz W, Wu G, Wang X. Data reconstruction with information granules: An augmented method of fuzzy clustering Applied Soft Computing. 55: 523-532. DOI: 10.1016/J.Asoc.2017.02.014 |
0.457 |
|
2017 |
Karczmarek P, Kiersztyn A, Pedrycz W. Generalized Choquet Integral for Face Recognition International Journal of Fuzzy Systems. 20: 1047-1055. DOI: 10.1007/S40815-017-0355-5 |
0.33 |
|
2017 |
Al-Hmouz R, Pedrycz W, Daqrouq K, Morfeq A. Development of Multimodal Biometric Systems with Three-Way and Fuzzy Set-Based Decision Mechanisms International Journal of Fuzzy Systems. 20: 128-140. DOI: 10.1007/S40815-017-0299-9 |
0.361 |
|
2016 |
Wang S, Pedrycz W. Data-Driven Adaptive Probabilistic Robust Optimization Using Information Granulation. Ieee Transactions On Cybernetics. PMID 28026796 DOI: 10.1109/Tcyb.2016.2638461 |
0.391 |
|
2016 |
Zhang X, Zhuang Y, Wang W, Pedrycz W. Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition. Ieee Transactions On Cybernetics. PMID 28026795 DOI: 10.1109/Tcyb.2016.2636370 |
0.329 |
|
2016 |
Zhu X, Pedrycz W, Li Z. Granular Data Description: Designing Ellipsoidal Information Granules. Ieee Transactions On Cybernetics. PMID 27740507 DOI: 10.1109/TCYB.2016.2612226 |
0.352 |
|
2016 |
Liu F, Pedrycz W, Zhang WG. Limited Rationality and Its Quantification Through the Interval Number Judgments With Permutations. Ieee Transactions On Cybernetics. PMID 27542190 DOI: 10.1109/Tcyb.2016.2594491 |
0.417 |
|
2016 |
Zhou N, Cheng H, Pedrycz W, Zhang Y, Liu H. Discriminative sparse subspace learning and its application to unsupervised feature selection. Isa Transactions. PMID 26803552 DOI: 10.1016/J.Isatra.2015.12.011 |
0.349 |
|
2016 |
Su SF, Pedrycz W, Hong TP, De Carvalho Fde A. Guest Editorial Special Issue on Granular/Symbolic Data Processing. Ieee Transactions On Cybernetics. 46: 342-3. PMID 26742157 DOI: 10.1109/Tcyb.2015.2513258 |
0.426 |
|
2016 |
Zjavka L, Pedrycz W. Constructing general partial differential equations using polynomial and neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 73: 58-69. PMID 26547244 DOI: 10.1016/J.Neunet.2015.10.001 |
0.337 |
|
2016 |
Zhao J, Han Z, Pedrycz W, Wang W. Granular Model of Long-Term Prediction for Energy System in Steel Industry. Ieee Transactions On Cybernetics. 46: 388-400. PMID 26168454 DOI: 10.1109/Tcyb.2015.2445918 |
0.332 |
|
2016 |
Talaska T, Kolasa M, Dlugosz R, Pedrycz W. Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology. Ieee Transactions On Neural Networks and Learning Systems. 27: 661-73. PMID 26087501 DOI: 10.1109/Tnnls.2015.2434847 |
0.307 |
|
2016 |
Li F, Zheng D, Zhao T, Pedrycz W. A novel approach for anomaly detection in data streams: Fuzzy-statistical detection mode Journal of Intelligent and Fuzzy Systems. 30: 2611-2622. DOI: 10.3233/Ifs-151910 |
0.42 |
|
2016 |
Liu C, Pedrycz W. Covering-based multi-granulation fuzzy rough sets Journal of Intelligent and Fuzzy Systems. 30: 303-318. DOI: 10.3233/Ifs-151757 |
0.477 |
|
2016 |
Pedrycz W. System Modeling With Fuzzy Models: Fundamental Developments And Perspectives Iranian Journal of Fuzzy Systems. 13: 1-14. DOI: 10.22111/Ijfs.2016.2940 |
0.432 |
|
2016 |
Wang Y, Dai Y, Chen YW, Pedrycz W. An interpretability-accuracy tradeoff in learning parameters of intuitionistic fuzzy rule-based systems Journal of Advanced Computational Intelligence and Intelligent Informatics. 20: 773-787. DOI: 10.20965/Jaciii.2016.P0773 |
0.359 |
|
2016 |
Homenda W, Jastrzebska A, Pedrycz W. Fuzzy cognitive map reconstruction: Dynamics versus history Applied Mathematics and Information Sciences. 10: 93-105. DOI: 10.18576/Amis/100109 |
0.359 |
|
2016 |
Homenda W, Pedrycz W. Automatic data understanding: The tool for intelligent man-machine communication Applied Mathematics and Information Sciences. 10: 49-61. DOI: 10.18576/Amis/100105 |
0.316 |
|
2016 |
Aliev RA, Pedrycz W, Huseynov OH, Eyupoglu SZ. Approximate Reasoning on a Basis of Z-number valued If-Then Rules Ieee Transactions On Fuzzy Systems. DOI: 10.1109/Tfuzz.2016.2612303 |
0.452 |
|
2016 |
Hu X, Pedrycz W, Wang X. Granular Fuzzy Rule-based Models: A Study in a Comprehensive Evaluation of Fuzzy Models Ieee Transactions On Fuzzy Systems. DOI: 10.1109/Tfuzz.2016.2612300 |
0.485 |
|
2016 |
Huang W, Oh SK, Pedrycz W. Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design Ieee Transactions On Fuzzy Systems. DOI: 10.1109/Tfuzz.2016.2612267 |
0.458 |
|
2016 |
Zhou J, Li X, Pedrycz W. Mean-Semi-Entropy Models of Fuzzy Portfolio Selection Ieee Transactions On Fuzzy Systems. 24: 1627-1636. DOI: 10.1109/Tfuzz.2016.2543753 |
0.473 |
|
2016 |
Livi L, Tahayori H, Rizzi A, Sadeghian A, Pedrycz W. Classification of type-2 fuzzy sets represented as sequences of vertical slices Ieee Transactions On Fuzzy Systems. 24: 1022-1034. DOI: 10.1109/Tfuzz.2015.2500274 |
0.439 |
|
2016 |
Pedrycz W, Wang X. Designing Fuzzy Sets With the Use of the Parametric Principle of Justifiable Granularity Ieee Transactions On Fuzzy Systems. 24: 489-496. DOI: 10.1109/Tfuzz.2015.2453393 |
0.405 |
|
2016 |
Pedrycz W, Jastrzebska A, Homenda W. Design of fuzzy cognitive maps for modeling time series Ieee Transactions On Fuzzy Systems. 24: 120-130. DOI: 10.1109/Tfuzz.2015.2428717 |
0.387 |
|
2016 |
Espin-Andrade RA, Gonzalez E, Pedrycz W, Fernandez E. An Interpretable Logical Theory: The case of Compensatory Fuzzy Logic International Journal of Computational Intelligence Systems. 9: 612-626. DOI: 10.1080/18756891.2016.1204111 |
0.317 |
|
2016 |
Pedrycz W. From Fuzzy Models to Granular Fuzzy Models International Journal of Computational Intelligence Systems. 9: 35-42. DOI: 10.1080/18756891.2016.1180818 |
0.442 |
|
2016 |
Qin J, Liu X, Pedrycz W. Multi-attribute group decision making based on Choquet integral under interval-valued intuitionistic fuzzy environment International Journal of Computational Intelligence Systems. 9: 133-152. DOI: 10.1080/18756891.2016.1146530 |
0.456 |
|
2016 |
Hu J, Pedrycz W, Wang G. A roughness measure of fuzzy sets from the perspective of distance International Journal of General Systems. 1-16. DOI: 10.1080/03081079.2015.1086580 |
0.424 |
|
2016 |
Oh SK, Kim WD, Pedrycz W. Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: Design and analysis International Journal of General Systems. 45: 434-454. DOI: 10.1080/03081079.2015.1072523 |
0.471 |
|
2016 |
Cabrerizo FJ, Pedrycz W, Pérez IJ, Alonso S, Herrera-Viedma E. Group Decision Making in Linguistic Contexts: An Information Granulation Approach Procedia Computer Science. 91: 715-724. DOI: 10.1016/J.Procs.2016.07.062 |
0.354 |
|
2016 |
Fan K, Pedrycz W. Opinion evolution influenced by informed agents Physica a: Statistical Mechanics and Its Applications. 462: 431-441. DOI: 10.1016/J.Physa.2016.06.110 |
0.332 |
|
2016 |
Ng WW, Zeng G, Zhang J, Yeung DS, Pedrycz W. Dual autoencoders features for imbalance classification problem Pattern Recognition. 60: 875-889. DOI: 10.1016/J.Patcog.2016.06.013 |
0.346 |
|
2016 |
Zhong C, Pedrycz W, Li Z, Wang D, Li L. Fuzzy associative memories: A design through fuzzy clustering Neurocomputing. 173: 1154-1162. DOI: 10.1016/J.Neucom.2015.08.072 |
0.406 |
|
2016 |
Feng F, Cho J, Pedrycz W, Fujita H, Herawan T. Soft set based association rule mining Knowledge-Based Systems. 111: 268-282. DOI: 10.1016/J.Knosys.2016.08.020 |
0.377 |
|
2016 |
Pham VN, Ngo LT, Pedrycz W. Interval-valued fuzzy set approach to fuzzy co-clustering for data classification Knowledge-Based Systems. 107: 1-13. DOI: 10.1016/J.Knosys.2016.05.049 |
0.442 |
|
2016 |
Protasiewicz J, Pedrycz W, Kozłowski M, Dadas S, Stanisławek T, Kopacz A, Gałȩzewska M. A recommender system of reviewers and experts in reviewing problems Knowledge-Based Systems. 106: 164-178. DOI: 10.1016/J.Knosys.2016.05.041 |
0.419 |
|
2016 |
Nguyen D, Nguyen LTT, Vo B, Pedrycz W. Efficient mining of class association rules with the itemset constraint Knowledge-Based Systems. 103: 73-88. DOI: 10.1016/J.Knosys.2016.03.025 |
0.4 |
|
2016 |
Wang X, Pedrycz W, Gacek A, Liu X. From numeric data to information granules: A design through clustering and the principle of justifiable granularity Knowledge-Based Systems. 101: 100-113. DOI: 10.1016/J.Knosys.2016.03.012 |
0.421 |
|
2016 |
Zhang L, Lu W, Liu X, Pedrycz W, Zhong C. Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values Knowledge-Based Systems. 99: 51-70. DOI: 10.1016/J.Knosys.2016.01.048 |
0.438 |
|
2016 |
Hu J, Pedrycz W, Wang G, Wang K. Rough sets in distributed decision information systems Knowledge-Based Systems. 94: 13-22. DOI: 10.1016/J.Knosys.2015.10.025 |
0.345 |
|
2016 |
Ekel P, Kokshenev I, Parreiras R, Pedrycz W, Pereira J. Multiobjective and multiattribute decision making in a fuzzy environment and their power engineering applications Information Sciences. 361: 100-119. DOI: 10.1016/J.Ins.2016.04.030 |
0.406 |
|
2016 |
Lei X, Wang F, Wu FX, Zhang A, Pedrycz W. Protein complex identification through Markov clustering with firefly algorithm on dynamic protein-protein interaction networks Information Sciences. 329: 303-316. DOI: 10.1016/J.Ins.2015.09.028 |
0.339 |
|
2016 |
Aliev RA, Pedrycz W, Kreinovich V, Huseynov OH. The general theory of decisions Information Sciences. 327: 125-148. DOI: 10.1016/J.Ins.2015.07.055 |
0.338 |
|
2016 |
Roh SB, Oh SK, Pedrycz W, Seo K. Development of autofocusing algorithm based on fuzzy transforms Fuzzy Sets and Systems. 288: 129-144. DOI: 10.1016/J.Fss.2015.08.029 |
0.368 |
|
2016 |
Kerr-Wilson J, Pedrycz W. Design of rule-based models through information granulation Expert Systems With Applications. 46: 274-285. DOI: 10.1016/J.Eswa.2015.10.030 |
0.453 |
|
2016 |
Ouyang Y, Pedrycz W. A new model for intuitionistic fuzzy multi-attributes decision making European Journal of Operational Research. 249: 677-682. DOI: 10.1016/J.Ejor.2015.08.043 |
0.456 |
|
2016 |
Zhong C, Pedrycz W, Wang D, Li L, Li Z. Granular data imputation: A framework of Granular Computing Applied Soft Computing Journal. 46: 307-316. DOI: 10.1016/J.Asoc.2016.05.006 |
0.463 |
|
2016 |
Zhou X, Pedrycz W, Kuang Y, Zhang Z. Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation Applied Soft Computing Journal. 46: 424-440. DOI: 10.1016/J.Asoc.2016.04.038 |
0.45 |
|
2016 |
Hu X, Pedrycz W, Wang X. Optimal allocation of information granularity in system modeling through the maximization of information specificity: A development of granular input space Applied Soft Computing Journal. 42: 410-422. DOI: 10.1016/J.Asoc.2016.02.001 |
0.484 |
|
2016 |
Lu W, Zhang L, Pedrycz W, Yang J, Liu X. The granular extension of Sugeno-type fuzzy models based on optimal allocation of information granularity and its application to forecasting of time series Applied Soft Computing Journal. 42: 38-52. DOI: 10.1016/J.Asoc.2016.01.021 |
0.435 |
|
2016 |
Qin J, Liu X, Pedrycz W. Frank aggregation operators and their application to hesitant fuzzy multiple attribute decision making Applied Soft Computing Journal. 41: 428-452. DOI: 10.1016/J.Asoc.2015.12.030 |
0.395 |
|
2016 |
Homenda W, Jastrzebska A, Pedrycz W. Multicriteria decision making inspired by human cognitive processes Applied Mathematics and Computation. 290: 392-411. DOI: 10.1016/J.Amc.2016.05.041 |
0.376 |
|
2016 |
Huang W, Oh SK, Pedrycz W. Hybrid fuzzy polynomial neural networks with the aid of weighted fuzzy clustering method and fuzzy polynomial neurons Applied Intelligence. 1-22. DOI: 10.1007/S10489-016-0844-5 |
0.492 |
|
2016 |
Nguyen H, Vo B, Nguyen M, Pedrycz W. An efficient algorithm for mining frequent weighted itemsets using interval word segments Applied Intelligence. 1-13. DOI: 10.1007/S10489-016-0799-6 |
0.327 |
|
2016 |
Al-Hmouz R, Pedrycz W. Models of time series with time granulation Knowledge and Information Systems. 48: 561-580. DOI: 10.1007/S10115-015-0868-X |
0.358 |
|
2016 |
Al-Hmouz R, Pedrycz W, Balamash AS, Morfeq A. Granular description of data in a non-stationary environment Soft Computing. 1-18. DOI: 10.1007/s00500-016-2352-2 |
0.343 |
|
2016 |
Isazadeh A, Mahan F, Pedrycz W. MFlexDT: multi flexible fuzzy decision tree for data stream classification Soft Computing. 20: 3719-3733. DOI: 10.1007/s00500-015-1733-2 |
0.305 |
|
2016 |
Wei Y, Watada J, Pedrycz W. Design of a qualitative classification model through fuzzy support vector machine with type-2 fuzzy expected regression classifier preset Ieej Transactions On Electrical and Electronic Engineering. 11: 348-356. DOI: 10.1002/Tee.22224 |
0.482 |
|
2016 |
Zhang L, Lu W, Liu X, Pedrycz W, Zhong C, Wang L. A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value International Journal of Intelligent Systems. 31: 297-313. DOI: 10.1002/Int.21752 |
0.39 |
|
2015 |
An S, Hu Q, Pedrycz W, Zhu P, Tsang EC. Data-Distribution-Aware Fuzzy Rough Set Model and its Application to Robust Classification. Ieee Transactions On Cybernetics. PMID 26584507 DOI: 10.1109/Tcyb.2015.2496425 |
0.464 |
|
2015 |
Yoo SH, Oh SK, Pedrycz W. Optimized face recognition algorithm using radial basis function neural networks and its practical applications. Neural Networks : the Official Journal of the International Neural Network Society. 69: 111-25. PMID 26163042 DOI: 10.1016/J.Neunet.2015.05.001 |
0.454 |
|
2015 |
Ding Y, Cheng L, Pedrycz W, Hao K. Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression. Ieee Transactions On Neural Networks and Learning Systems. PMID 25974954 DOI: 10.1109/Tnnls.2015.2426182 |
0.408 |
|
2015 |
Livi L, Sadeghian A, Pedrycz W. Entropic One-Class Classifiers. Ieee Transactions On Neural Networks and Learning Systems. 26: 3187-200. PMID 25879977 DOI: 10.1109/Tnnls.2015.2418332 |
0.405 |
|
2015 |
Chalmers E, Pedrycz W, Lou E. Human experts' and a fuzzy model's predictions of outcomes of scoliosis treatment: a comparative analysis. Ieee Transactions On Bio-Medical Engineering. 62: 1001-7. PMID 25494498 DOI: 10.1109/Tbme.2014.2377594 |
0.31 |
|
2015 |
Pedrycz W. Concepts and Design Aspects of Granular Models of Type-1 and Type-2 The International Journal of Fuzzy Logic and Intelligent Systems. 15: 87-95. DOI: 10.5391/Ijfis.2015.15.2.87 |
0.398 |
|
2015 |
Pedrycz W. Granular fuzzy rule-based architectures: Pursuing analysis and design in the framework of granular computing Intelligent Decision Technologies. 9: 321-330. DOI: 10.3233/Idt-140227 |
0.473 |
|
2015 |
Acampora G, Pedrycz W, Vitiello A. A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps Ieee Transactions On Fuzzy Systems. 23: 2397-2411. DOI: 10.1109/Tfuzz.2015.2426311 |
0.401 |
|
2015 |
Pedrycz W, Al-Hmouz R, Balamash AS, Morfeq A. Hierarchical Granular Clustering: An Emergence of Information Granules of Higher Type and Higher Order Ieee Transactions On Fuzzy Systems. 23: 2270-2283. DOI: 10.1109/Tfuzz.2015.2417896 |
0.486 |
|
2015 |
Behbood V, Lu J, Zhang G, Pedrycz W. Multistep Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction Ieee Transactions On Fuzzy Systems. 23: 1917-1935. DOI: 10.1109/Tfuzz.2014.2387872 |
0.39 |
|
2015 |
Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W. A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning Ieee Transactions On Fuzzy Systems. 23: 1638-1654. DOI: 10.1109/Tfuzz.2014.2371479 |
0.445 |
|
2015 |
Wang S, Pedrycz W. Robust Granular Optimization: A Structured Approach for Optimization under Integrated Uncertainty Ieee Transactions On Fuzzy Systems. 23: 1372-1386. DOI: 10.1109/Tfuzz.2014.2360941 |
0.351 |
|
2015 |
Gacek A, Pedrycz W. Clustering Granular Data and Their Characterization with Information Granules of Higher Type Ieee Transactions On Fuzzy Systems. 23: 850-860. DOI: 10.1109/Tfuzz.2014.2329707 |
0.345 |
|
2015 |
Qin J, Chu J, Liu X, Pedrycz W. Approaches to interval type-2 fuzzy multiple attribute group decision making based on grey incidence analysis and FTP utility function Ieee International Conference On Fuzzy Systems. 2015. DOI: 10.1109/FUZZ-IEEE.2015.7337823 |
0.368 |
|
2015 |
Espin-Andrade RA, Caballero EG, Pedrycz W, Fernández González ER. Archimedean-Compensatory Fuzzy Logic Systems International Journal of Computational Intelligence Systems. 8: 54-62. DOI: 10.1080/18756891.2015.1129591 |
0.445 |
|
2015 |
Pedrycz W, Gacek A, Wang X. Clustering in augmented space of granular constraints: A study in knowledge-based clustering Pattern Recognition Letters. 67: 122-129. DOI: 10.1016/J.Patrec.2015.08.019 |
0.41 |
|
2015 |
Zhou N, Xu Y, Cheng H, Fang J, Pedrycz W. Global and local structure preserving sparse subspace learning: An iterative approach to unsupervised feature selection Pattern Recognition. DOI: 10.1016/J.Patcog.2015.12.008 |
0.36 |
|
2015 |
Wang S, Pedrycz W, Zhu Q, Zhu W. Subspace learning for unsupervised feature selection via matrix factorization Pattern Recognition. 48: 10-19. DOI: 10.1016/J.Patcog.2014.08.004 |
0.343 |
|
2015 |
Wang X, Liu X, Pedrycz W, Zhang L. Fuzzy rule based decision trees Pattern Recognition. 48: 50-59. DOI: 10.1016/J.Patcog.2014.08.001 |
0.423 |
|
2015 |
Reyes-Galaviz OF, Pedrycz W. Granular fuzzy modeling with evolving hyperboxes in multi-dimensional space of numerical data Neurocomputing. 168: 240-253. DOI: 10.1016/J.Neucom.2015.05.102 |
0.486 |
|
2015 |
Qin J, Liu X, Pedrycz W. An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment Knowledge-Based Systems. 86: 116-130. DOI: 10.1016/J.Knosys.2015.05.025 |
0.467 |
|
2015 |
Pedrycz W, Succi G, Sillitti A, Iljazi J. Data description: A general framework of information granules Knowledge-Based Systems. 80: 98-108. DOI: 10.1016/J.Knosys.2014.12.030 |
0.417 |
|
2015 |
Pedrycz W, Al-Hmouz R, Balamash AS, Morfeq A. Designing granular fuzzy models: A hierarchical approach to fuzzy modeling Knowledge-Based Systems. 76: 42-52. DOI: 10.1016/J.Knosys.2014.11.025 |
0.473 |
|
2015 |
Wang S, Pedrycz W, Zhu Q, Zhu W. Unsupervised feature selection via maximum projection and minimum redundancy Knowledge-Based Systems. 75: 19-29. DOI: 10.1016/J.Knosys.2014.11.008 |
0.366 |
|
2015 |
Dong R, Pedrycz W. Approximation grid evaluation-based PID control in cascade with nonlinear gain Journal of the Franklin Institute. 352: 4279-4296. DOI: 10.1016/J.Jfranklin.2015.06.018 |
0.307 |
|
2015 |
Li J, Pedrycz W, Wang X. A rule-based development of incremental models International Journal of Approximate Reasoning. 64: 20-38. DOI: 10.1016/J.Ijar.2015.06.007 |
0.486 |
|
2015 |
Reyes-Galaviz OF, Pedrycz W. Granular fuzzy models: Analysis, design, and evaluation International Journal of Approximate Reasoning. 64: 1-19. DOI: 10.1016/J.Ijar.2015.06.005 |
0.462 |
|
2015 |
Lu W, Chen X, Pedrycz W, Liu X, Yang J. Using interval information granules to improve forecasting in fuzzy time series International Journal of Approximate Reasoning. 57: 1-18. DOI: 10.1016/J.Ijar.2014.11.002 |
0.435 |
|
2015 |
Nguyen DD, Ngo LT, Pham LT, Pedrycz W. Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued Fuzzy C-Means algorithms Fuzzy Sets and Systems. 279: 17-39. DOI: 10.1016/J.Fss.2015.01.020 |
0.487 |
|
2015 |
Portmann E, Meier A, Cudré-Mauroux P, Pedrycz W. FORA - A fuzzy set based framework for online reputation management Fuzzy Sets and Systems. 269: 90-114. DOI: 10.1016/J.Fss.2014.06.004 |
0.453 |
|
2015 |
Pedrycz W. From Numeric Models to Granular System Modeling Fuzzy Information and Engineering. 7: 1-13. DOI: 10.1016/J.Fiae.2015.03.001 |
0.399 |
|
2015 |
Al-Hmouz R, Pedrycz W, Balamash A. Description and prediction of time series: A general framework of Granular Computing Expert Systems With Applications. 42: 4830-4839. DOI: 10.1016/J.Eswa.2015.01.060 |
0.349 |
|
2015 |
Balamash A, Pedrycz W, Al-Hmouz R, Morfeq A. An expansion of fuzzy information granules through successive refinements of their information content and their use to system modeling Expert Systems With Applications. 42: 2985-2997. DOI: 10.1016/J.Eswa.2014.11.027 |
0.432 |
|
2015 |
Chen J, Pedrycz W, Ha M, Ma L. Set-valued samples based support vector regression and its applications Expert Systems With Applications. 42: 2502-2509. DOI: 10.1016/J.Eswa.2014.09.038 |
0.351 |
|
2015 |
Wang W, Pedrycz W, Liu X. Time series long-term forecasting model based on information granules and fuzzy clustering Engineering Applications of Artificial Intelligence. 41: 17-24. DOI: 10.1016/J.Engappai.2015.01.006 |
0.409 |
|
2015 |
Izakian H, Pedrycz W, Jamal I. Fuzzy clustering of time series data using dynamic time warping distance Engineering Applications of Artificial Intelligence. 39: 235-244. DOI: 10.1016/J.Engappai.2014.12.015 |
0.411 |
|
2015 |
Sheri AM, Rafique A, Pedrycz W, Jeon M. Contrastive divergence for memristor-based restricted Boltzmann machine Engineering Applications of Artificial Intelligence. 37: 336-342. DOI: 10.1016/J.Engappai.2014.09.013 |
0.303 |
|
2015 |
Ngo LT, Mai DS, Pedrycz W. Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection Computers and Geosciences. 83: 1-16. DOI: 10.1016/J.Cageo.2015.06.011 |
0.414 |
|
2015 |
Wu G, Pedrycz W, Suganthan PN, Mallipeddi R. A variable reduction strategy for evolutionary algorithms handling equality constraints Applied Soft Computing Journal. 37: 774-786. DOI: 10.1016/J.Asoc.2015.09.007 |
0.326 |
|
2015 |
Giacomin PAS, Hemerly EM, Pedrycz W. A probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles Applied Soft Computing Journal. 34: 26-38. DOI: 10.1016/J.Asoc.2015.04.021 |
0.311 |
|
2015 |
Liang D, Pedrycz W, Liu D, Hu P. Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making Applied Soft Computing Journal. 29: 256-269. DOI: 10.1016/J.Asoc.2015.01.008 |
0.348 |
|
2015 |
Liu X, Wang X, Pedrycz W. Fuzzy clustering with semantic interpretation Applied Soft Computing Journal. 26: 21-30. DOI: 10.1016/J.Asoc.2014.09.037 |
0.427 |
|
2015 |
Qin J, Liu X, Pedrycz W. Hesitant Fuzzy Maclaurin Symmetric Mean Operators and Its Application to Multiple-Attribute Decision Making International Journal of Fuzzy Systems. 17: 509-520. DOI: 10.1007/S40815-015-0049-9 |
0.437 |
|
2015 |
Song M, Shang W, Wang L, Pedrycz W. Analysis of spatiotemporal data relationship using information granules International Journal of Machine Learning and Cybernetics. 8: 1439-1446. DOI: 10.1007/S13042-015-0386-X |
0.618 |
|
2015 |
He ZM, Chan PPK, Yeung DS, Pedrycz W, Ng WWY. Quantification of side-channel information leaks based on data complexity measures for web browsing International Journal of Machine Learning and Cybernetics. 6: 607-619. DOI: 10.1007/S13042-015-0348-3 |
0.33 |
|
2015 |
Pedrycz W, Al-Hmouz R, Balamash AS, Morfeq A. Modeling with linguistic entities and linguistic descriptors: a perspective of granular computing Soft Computing. DOI: 10.1007/s00500-015-1884-1 |
0.399 |
|
2015 |
Pedrycz W, Li K, Reformat M. Evolutionary reduction of fuzzy rule-based models Studies in Fuzziness and Soft Computing. 326: 459-481. DOI: 10.1007/978-3-319-19683-1_23 |
0.38 |
|
2015 |
Prokopowicz P, Pedrycz W. The directed compatibility between ordered fuzzy numbers-A base tool for a direction sensitive fuzzy information processing Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 9119: 249-259. DOI: 10.1007/978-3-319-19324-3_23 |
0.366 |
|
2015 |
Homenda W, Jastrzębska A, Pedrycz W. Nodes selection criteria for fuzzy cognitive maps designed to model time series Advances in Intelligent Systems and Computing. 323: 859-870. DOI: 10.1007/978-3-319-11310-4_75 |
0.385 |
|
2015 |
Pedrycz W. Fuzzy sets of higher type and higher order in fuzzy modeling Frontiers of Higher Order Fuzzy Sets. 31-49. DOI: 10.1007/978-1-4614-3442-9_3 |
0.379 |
|
2015 |
Lu W, Zhang L, Liu X, Yang J, Pedrycz W. A human-computer cooperation fuzzy c-means clustering with interval-valued weights International Journal of Intelligent Systems. 30: 81-98. DOI: 10.1002/Int.21683 |
0.402 |
|
2015 |
Feng F, Pedrycz W. On scalar products and decomposition theorems of fuzzy soft sets Journal of Multiple-Valued Logic and Soft Computing. 25: 45-80. |
0.358 |
|
2014 |
Huang W, Oh SK, Pedrycz W. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs). Neural Networks : the Official Journal of the International Neural Network Society. 60: 166-81. PMID 25233483 DOI: 10.1016/J.Neunet.2014.08.007 |
0.477 |
|
2014 |
Wang S, Watada J, Pedrycz W. Granular robust mean-CVaR feedstock flow planning for waste-to-energy systems under integrated uncertainty. Ieee Transactions On Cybernetics. 44: 1846-57. PMID 25222726 DOI: 10.1109/Tcyb.2013.2296500 |
0.386 |
|
2014 |
Xu X, Huang Z, Graves D, Pedrycz W. A clustering-based graph Laplacian framework for value function approximation in reinforcement learning. Ieee Transactions On Cybernetics. 44: 2613-25. PMID 24802018 DOI: 10.1109/Tcyb.2014.2311578 |
0.37 |
|
2014 |
Wu G, Pedrycz W, Ma M, Qiu D, Li H, Liu J. A particle swarm optimization variant with an inner variable learning strategy. Thescientificworldjournal. 2014: 713490. PMID 24587746 DOI: 10.1155/2014/713490 |
0.341 |
|
2014 |
Zhang H, Pedrycz W, Miao D, Wei Z. From principal curves to granular principal curves. Ieee Transactions On Cybernetics. 44: 748-60. PMID 23996588 DOI: 10.1109/Tcyb.2013.2270294 |
0.484 |
|
2014 |
Izakian H, Pedrycz W. Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach Ieee Transactions On Fuzzy Systems. 22: 1612-1624. DOI: 10.1109/Tfuzz.2014.2302456 |
0.404 |
|
2014 |
Pedrycz W, Izakian H. Cluster-Centric Fuzzy Modeling Ieee Transactions On Fuzzy Systems. 22: 1585-1597. DOI: 10.1109/Tfuzz.2014.2300134 |
0.503 |
|
2014 |
Pedrycz W, Homenda W. From fuzzy cognitive maps to granular cognitive maps Ieee Transactions On Fuzzy Systems. 22: 859-869. DOI: 10.1109/Tfuzz.2013.2277730 |
0.425 |
|
2014 |
Chen J, Pedrycz W, Ma L, Wang C. A new information security risk analysis method based on membership degree Kybernetes. 43: 686-698. DOI: 10.1108/K-10-2013-0235 |
0.36 |
|
2014 |
Izakian H, Pedrycz W. Agreement-based fuzzy C-means for clustering data with blocks of features Neurocomputing. 127: 266-280. DOI: 10.1016/J.Neucom.2013.08.006 |
0.449 |
|
2014 |
Lu W, Yang J, Liu X, Pedrycz W. The modeling and prediction of time series based on synergy of high-order fuzzy cognitive map and fuzzy c-means clustering Knowledge-Based Systems. 70: 242-255. DOI: 10.1016/J.Knosys.2014.07.004 |
0.449 |
|
2014 |
Pedrycz W, Al-Hmouz R, Morfeq A, Balamash AS. Building granular fuzzy decision support systems Knowledge-Based Systems. 58: 3-10. DOI: 10.1016/J.Knosys.2013.07.022 |
0.436 |
|
2014 |
Park J, Jeon M, Pedrycz W. Spectral clustering with physical intuition on spring–mass dynamics Journal of the Franklin Institute. 351: 3245-3268. DOI: 10.1016/J.Jfranklin.2014.02.017 |
0.325 |
|
2014 |
Cimino MGCA, Lazzerini B, Marcelloni F, Pedrycz W. Genetic interval neural networks for granular data regression Information Sciences. 257: 313-330. DOI: 10.1016/J.Ins.2012.12.049 |
0.426 |
|
2014 |
Herrera-Viedma E, Cabrerizo FJ, Kacprzyk J, Pedrycz W. A review of soft consensus models in a fuzzy environment Information Fusion. 17: 4-13. DOI: 10.1016/J.Inffus.2013.04.002 |
0.379 |
|
2014 |
Pedrycz W, Song M. A granulation of linguistic information in AHP decision-making problems Information Fusion. 17: 93-101. DOI: 10.1016/J.Inffus.2011.09.003 |
0.589 |
|
2014 |
Acampora G, Pedrycz W, Vasilakos AV. Efficient modeling of MIMO systems through Timed Automata based Neuro-Fuzzy Inference Engine International Journal of Approximate Reasoning. 55: 1336-1356. DOI: 10.1016/J.Ijar.2014.02.003 |
0.447 |
|
2014 |
Nguyen CH, Huynh VN, Pedrycz W. A construction of sound semantic linguistic scales using 4-tuple representation of term semantics International Journal of Approximate Reasoning. 55: 763-786. DOI: 10.1016/J.Ijar.2013.10.012 |
0.35 |
|
2014 |
Kerr-Wilson J, Pedrycz W. Some new qualitative insights into quality of fuzzy rule-based models Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2016.05.002 |
0.441 |
|
2014 |
Hu X, Pedrycz W, Castillo O, Melin P. Fuzzy rule-based models with interactive rules and their granular generalization Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2016.03.005 |
0.477 |
|
2014 |
Oh SK, Yoo SH, Pedrycz W. A comparative study of feature extraction methods and their application to P-RBF NNs in face recognition problem Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2015.11.018 |
0.369 |
|
2014 |
Pedrycz W. From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis Fuzzy Sets and Systems. DOI: 10.1016/J.Fss.2014.04.017 |
0.457 |
|
2014 |
Cabrerizo FJ, Ureña R, Pedrycz W, Herrera-Viedma E. Building consensus in group decision making with an allocation of information granularity Fuzzy Sets and Systems. 255: 115-127. DOI: 10.1016/J.Fss.2014.03.016 |
0.407 |
|
2014 |
Oh SK, Kim WD, Pedrycz W, Seo K. Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization Fuzzy Sets and Systems. 237: 96-117. DOI: 10.1016/J.Fss.2013.08.011 |
0.519 |
|
2014 |
Isazadeh A, Pedrycz W, Mahan F. ECA rule learning in dynamic environments Expert Systems With Applications. 41: 7847-7857. DOI: 10.1016/J.Eswa.2014.06.028 |
0.392 |
|
2014 |
Roh SB, Pedrycz W, Ahn TC. A design of granular fuzzy classifier Expert Systems With Applications. 41: 6786-6795. DOI: 10.1016/J.Eswa.2014.04.040 |
0.437 |
|
2014 |
Zhang L, Pedrycz W, Lu W, Liu X. An interval weighed fuzzy c-means clustering by genetically guided alternating optimization Expert Systems With Applications. 41: 5960-5971. DOI: 10.1016/J.Eswa.2014.03.042 |
0.394 |
|
2014 |
Lu W, Pedrycz W, Liu X, Yang J, Li P. The modeling of time series based on fuzzy information granules Expert Systems With Applications. 41: 3799-3808. DOI: 10.1016/J.Eswa.2013.12.005 |
0.462 |
|
2014 |
Wang L, Liu X, Pedrycz W, Shao Y. Determination of temporal information granules to improve forecasting in fuzzy time series Expert Systems With Applications. 41: 3134-3142. DOI: 10.1016/J.Eswa.2013.10.046 |
0.405 |
|
2014 |
Yu Y, Pedrycz W, Miao D. Multi-label classification by exploiting label correlations Expert Systems With Applications. 41: 2989-3004. DOI: 10.1016/J.Eswa.2013.10.030 |
0.319 |
|
2014 |
Pedrycz W. Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing European Journal of Operational Research. 232: 137-145. DOI: 10.1016/J.Ejor.2012.03.038 |
0.423 |
|
2014 |
Zaniewski K, Pedrycz W. A hybrid optimization approach to conformance testing of finite automata Applied Soft Computing Journal. 23: 91-103. DOI: 10.1016/J.Asoc.2014.05.018 |
0.336 |
|
2014 |
Wang X, Pedrycz W, Niu R. Spatio-temporal analysis of Quaternary deposit landslides in the Three Gorges Natural Hazards. 75: 2793-2813. DOI: 10.1007/S11069-014-1462-3 |
0.304 |
|
2014 |
Russo B, Succi G, Pedrycz W. Mining system logs to learn error predictors: a case study of a telemetry system Empirical Software Engineering. 20: 879-927. DOI: 10.1007/S10664-014-9303-2 |
0.308 |
|
2014 |
Rajati MR, Khaloozadeh H, Pedrycz W. Fuzzy logic and self-referential reasoning: A comparative study with some new concepts Artificial Intelligence Review. 41: 331-357. DOI: 10.1007/S10462-011-9311-1 |
0.419 |
|
2014 |
Homenda W, Pedrycz W. Automatic data understanding a linguistic tool for granular cognitive maps designing Advances in Intelligent Systems and Computing. 322: 217-228. DOI: 10.1007/978-3-319-11313-5_21 |
0.334 |
|
2014 |
Aliev R, Pedrycz W, Zeinalova LM, Huseynov OH. Decision making with second-order imprecise probabilities International Journal of Intelligent Systems. 29: 137-160. DOI: 10.1002/Int.21630 |
0.364 |
|
2013 |
Song M, Pedrycz W. Granular neural networks: concepts and development schemes. Ieee Transactions On Neural Networks and Learning Systems. 24: 542-53. PMID 24808376 DOI: 10.1109/Tnnls.2013.2237787 |
0.632 |
|
2013 |
Pedrycz W, Al-Hmouz R, Morfeq A, Balamash A. The design of free structure granular mappings: the use of the principle of justifiable granularity. Ieee Transactions On Cybernetics. 43: 2105-13. PMID 23757519 DOI: 10.1109/Tcyb.2013.2240384 |
0.443 |
|
2013 |
Pedrycz W. Associations Among Information Granules and Their Optimization in Granulation-Degranulation Mechanism of Granular Computing International Journal of Fuzzy Logic and Intelligent Systems. 13: 245-253. DOI: 10.5391/Ijfis.2013.13.4.245 |
0.378 |
|
2013 |
Pedrycz W. From Numeric to Granular Description and Interpretation of Information Granules Fundamenta Informaticae. 127: 399-412. DOI: 10.3233/Fi-2013-917 |
0.477 |
|
2013 |
ALIEV RA, PEDRYCZ W, HUSEYNOV OH. BEHAVIORAL DECISION MAKING WITH COMBINED STATES UNDER IMPERFECT INFORMATION International Journal of Information Technology & Decision Making. 12: 619-645. DOI: 10.1142/S0219622013500235 |
0.362 |
|
2013 |
PEDRYCZ W. KNOWLEDGE MANAGEMENT AND SEMANTIC MODELING: A ROLE OF INFORMATION GRANULARITY International Journal of Software Engineering and Knowledge Engineering. 23: 5-11. DOI: 10.1142/S0218194013400019 |
0.321 |
|
2013 |
Tahayori H, Sadeghian A, Pedrycz W. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness Ieee Transactions On Fuzzy Systems. 21: 937-949. DOI: 10.1109/Tfuzz.2012.2236843 |
0.467 |
|
2013 |
Pedrycz W. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features Ieee Transactions On Fuzzy Systems. 21: 978-982. DOI: 10.1109/Tfuzz.2012.2236842 |
0.347 |
|
2013 |
Izakian H, Pedrycz W, Jamal I. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means Ieee Transactions On Fuzzy Systems. 21: 855-868. DOI: 10.1109/Tfuzz.2012.2233479 |
0.387 |
|
2013 |
Kim WD, Oh SK, Seo KS, Pedrycz W. A design of FCM-based interval type-2 fuzzy neural network classifier with the aid of PSO Proceedings of the 2013 Joint Ifsa World Congress and Nafips Annual Meeting, Ifsa/Nafips 2013. 1209-1214. DOI: 10.1109/IFSA-NAFIPS.2013.6608573 |
0.365 |
|
2013 |
Tsehayae AA, Pedrycz W, Fayek AR. Application of granular fuzzy modeling for abstracting labour productivity knowledge bases Proceedings of the 2013 Joint Ifsa World Congress and Nafips Annual Meeting, Ifsa/Nafips 2013. 1096-1101. DOI: 10.1109/IFSA-NAFIPS.2013.6608553 |
0.323 |
|
2013 |
Kim WD, Oh SK, Seo KS, Pedrycz W. Growing rule-based fuzzy model developed with the aid of fuzzy clustering Proceedings of the 2013 Joint Ifsa World Congress and Nafips Annual Meeting, Ifsa/Nafips 2013. 573-578. DOI: 10.1109/IFSA-NAFIPS.2013.6608464 |
0.39 |
|
2013 |
Zhang H, Pedrycz W, Miao D, Zhong C. A global structure-based algorithm for detecting the principal graph from complex data Pattern Recognition. 46: 1638-1647. DOI: 10.1016/J.Patcog.2012.11.015 |
0.319 |
|
2013 |
Oh S, Kim W, Park B, Pedrycz W. A design of granular-oriented self-organizing hybrid fuzzy polynomial neural networks Neurocomputing. 119: 292-307. DOI: 10.1016/J.Neucom.2013.03.029 |
0.46 |
|
2013 |
Bereta M, Pedrycz W, Reformat M. Local descriptors and similarity measures for frontal face recognition: A comparative analysis Journal of Visual Communication and Image Representation. 24: 1213-1231. DOI: 10.1016/J.Jvcir.2013.08.004 |
0.312 |
|
2013 |
Huang W, Oh S, Pedrycz W. A fuzzy time-dependent project scheduling problem Information Sciences. 246: 100-114. DOI: 10.1016/J.Ins.2013.05.026 |
0.416 |
|
2013 |
Park B, Oh S, Pedrycz W. The design of polynomial function-based neural network predictors for detection of software defects Information Sciences. 229: 40-57. DOI: 10.1016/J.Ins.2011.01.026 |
0.464 |
|
2013 |
Yu Y, Pedrycz W, Miao D. Neighborhood rough sets based multi-label classification for automatic image annotation International Journal of Approximate Reasoning. 54: 1373-1387. DOI: 10.1016/J.Ijar.2013.06.003 |
0.371 |
|
2013 |
Liang D, Liu D, Pedrycz W, Hu P. Triangular fuzzy decision-theoretic rough sets International Journal of Approximate Reasoning. 54: 1087-1106. DOI: 10.1016/J.Ijar.2013.03.014 |
0.419 |
|
2013 |
Nguyen CH, Pedrycz W, Duong TL, Tran TS. A genetic design of linguistic terms for fuzzy rule based classifiers International Journal of Approximate Reasoning. 54: 1-21. DOI: 10.1016/J.Ijar.2012.07.007 |
0.467 |
|
2013 |
Wang L, Liu X, Pedrycz W. Effective intervals determined by information granules to improve forecasting in fuzzy time series Expert Systems With Applications. 40: 5673-5679. DOI: 10.1016/J.Eswa.2013.04.026 |
0.446 |
|
2013 |
Bereta M, Pedrycz W, Reformat M. Analysis and design of rank-based classifiers Expert Systems With Applications. 40: 3256-3265. DOI: 10.1016/J.Eswa.2012.12.038 |
0.324 |
|
2013 |
Oh S, Yoo S, Pedrycz W. Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks : Design and its application Expert Systems With Applications. 40: 1451-1466. DOI: 10.1016/J.Eswa.2012.08.046 |
0.474 |
|
2013 |
Cabrerizo FJ, Herrera-Viedma E, Pedrycz W. A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts European Journal of Operational Research. 230: 624-633. DOI: 10.1016/J.Ejor.2013.04.046 |
0.387 |
|
2013 |
Liu X, Feng X, Pedrycz W. Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach Data & Knowledge Engineering. 84: 1-25. DOI: 10.1016/j.datak.2012.12.001 |
0.341 |
|
2013 |
Pedrycz W, Homenda W. Building the fundamentals of granular computing: A principle of justifiable granularity Applied Soft Computing. 13: 4209-4218. DOI: 10.1016/J.Asoc.2013.06.017 |
0.427 |
|
2013 |
Huang W, Oh S, Guo Z, Pedrycz W. A space search optimization algorithm with accelerated convergence strategies Applied Soft Computing. 13: 4659-4675. DOI: 10.1016/J.Asoc.2013.06.005 |
0.333 |
|
2013 |
Pedrycz W. Granular Computing as a Framework of System Modeling Journal of Control, Automation and Electrical Systems. 24: 81-86. DOI: 10.1007/S40313-013-0010-9 |
0.399 |
|
2013 |
Aliev RA, Pedrycz W, Alizadeh AV, Huseynov OH. Fuzzy optimality based decision making under imperfect information without utility Fuzzy Optimization and Decision Making. 12: 357-372. DOI: 10.1007/S10700-013-9160-2 |
0.434 |
|
2013 |
Park B, Kim W, Oh S, Pedrycz W. Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization Knowledge and Information Systems. 39: 207-240. DOI: 10.1007/S10115-012-0610-X |
0.475 |
|
2013 |
Zhai K, Jiang N, Pedrycz W. Cost prediction method based on an improved fuzzy model The International Journal of Advanced Manufacturing Technology. 65: 1045-1053. DOI: 10.1007/S00170-012-4238-5 |
0.403 |
|
2013 |
Azhar Ramli A, Watada J, Pedrycz W. A combination of genetic algorithm-based fuzzy C-means with a convex hull-based regression for real-time fuzzy switching regression analysis: application to industrial intelligent data analysis Ieej Transactions On Electrical and Electronic Engineering. 9: 71-82. DOI: 10.1002/Tee.21938 |
0.469 |
|
2012 |
Izakian H, Pedrycz W. A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection. Swarm and Evolutionary Computation. 4: 1-11. PMID 32288990 DOI: 10.1016/J.Swevo.2012.02.001 |
0.324 |
|
2012 |
Zhao J, Liu Q, Wang W, Pedrycz W, Cong L. Hybrid neural prediction and optimized adjustment for coke oven gas system in steel industry. Ieee Transactions On Neural Networks and Learning Systems. 23: 439-50. PMID 24808550 DOI: 10.1109/Tnnls.2011.2179309 |
0.326 |
|
2012 |
Kim I, Watada J, Pedrycz W, Wu JY. Pattern clustering with statistical methods using a DNA-based algorithm. Ieee Transactions On Nanobioscience. 11: 100-10. PMID 22665391 DOI: 10.1109/Tnb.2012.2190618 |
0.325 |
|
2012 |
Pedrycz W, Bargiela A. An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 42: 582-90. PMID 22067434 DOI: 10.1109/TSMCB.2011.2170067 |
0.356 |
|
2012 |
Gacek A, Pedrycz W. A characterization of electrocardiogram signals through optimal allocation of information granularity. Artificial Intelligence in Medicine. 54: 125-34. PMID 22000296 DOI: 10.1016/J.Artmed.2011.09.007 |
0.407 |
|
2012 |
Pedrycz W. Fuzzy neural networks with reference neurons as pattern classifiers. Ieee Transactions On Neural Networks. 3: 770-5. PMID 18276475 DOI: 10.1109/72.159065 |
0.333 |
|
2012 |
Pedrycz W, Waletzky J. Fuzzy clustering with partial supervision. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 27: 787-95. PMID 18263089 DOI: 10.1109/3477.623232 |
0.355 |
|
2012 |
Pedrycz W, de Oliveira JV. Optimization of fuzzy models. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 26: 627-36. PMID 18263061 DOI: 10.1109/3477.517038 |
0.34 |
|
2012 |
Pedrycz W. Shadowed sets: representing and processing fuzzy sets. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 28: 103-9. PMID 18255928 DOI: 10.1109/3477.658584 |
0.346 |
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2012 |
Pedrycz W, Waletzky J. Neural-network front ends in unsupervised learning. Ieee Transactions On Neural Networks. 8: 390-401. PMID 18255641 DOI: 10.1109/72.557690 |
0.369 |
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2012 |
Pedrycz W, Hirota K, Sessa S. A decomposition of fuzzy relations. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 31: 657-63. PMID 18244830 DOI: 10.1109/3477.938269 |
0.381 |
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2012 |
Pedrycz W, Vukovich G. Abstraction and specialization of information granules. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 31: 106-11. PMID 18244771 DOI: 10.1109/3477.907568 |
0.352 |
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2012 |
Bargiela A, Pedrycz W. Recursive information granulation: aggregation and interpretation issues. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 33: 96-112. PMID 18238160 DOI: 10.1109/TSMCB.2003.808190 |
0.301 |
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2012 |
Ramli AA, Watada J, Pedrycz W. An efficient solution of real-time fuzzy regression analysis to information granules problem Journal of Advanced Computational Intelligence and Intelligent Informatics. 16: 199-209. DOI: 10.20965/Jaciii.2012.P0199 |
0.392 |
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2012 |
Pizzi NJ, Pedrycz W. Classifying high-dimensional patterns using a fuzzy logic discriminant network Advances in Fuzzy Systems. DOI: 10.1155/2012/920920 |
0.389 |
|
2012 |
Ahmad SSS, Pedrycz W. Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization Applied Computational Intelligence and Soft Computing. 2012: 1-21. DOI: 10.1155/2012/347157 |
0.381 |
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2012 |
ALIEV RA, PEDRYCZ W, HUSEYNOV OH. DECISION THEORY WITH IMPRECISE PROBABILITIES International Journal of Information Technology & Decision Making. 11: 271-306. DOI: 10.1142/S0219622012400032 |
0.434 |
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2012 |
Breve F, Zhao L, Quiles M, Pedrycz W, Liu J. Particle Competition and Cooperation in Networks for Semi-Supervised Learning Ieee Transactions On Knowledge and Data Engineering. 24: 1686-1698. DOI: 10.1109/Tkde.2011.119 |
0.353 |
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2012 |
Coletta LFS, Vendramin L, Hruschka ER, Campello RJGB, Pedrycz W. Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines Ieee Transactions On Fuzzy Systems. 20: 444-462. DOI: 10.1109/Tfuzz.2011.2175400 |
0.399 |
|
2012 |
Graves D, Noppen J, Pedrycz W. Clustering with proximity knowledge and relational knowledge Pattern Recognition. 45: 2633-2644. DOI: 10.1016/J.Patcog.2011.12.019 |
0.366 |
|
2012 |
Oh S, Kim W, Pedrycz W, Joo S. Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution Neurocomputing. 78: 121-132. DOI: 10.1016/J.Neucom.2011.06.031 |
0.366 |
|
2012 |
Mitra S, Kundu PP, Pedrycz W. Feature selection using structural similarity Information Sciences. 198: 48-61. DOI: 10.1016/J.Ins.2012.02.042 |
0.385 |
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2012 |
Aliev R, Pedrycz W, Fazlollahi B, Huseynov O, Alizadeh A, Guirimov B. Fuzzy logic-based generalized decision theory with imperfect information Information Sciences. 189: 18-42. DOI: 10.1016/J.Ins.2011.11.027 |
0.389 |
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2012 |
Pedrycz W, Song M. Granular fuzzy models: a study in knowledge management in fuzzy modeling International Journal of Approximate Reasoning. 53: 1061-1079. DOI: 10.1016/J.Ijar.2012.05.002 |
0.619 |
|
2012 |
Pedrycz A, Hirota K, Pedrycz W, Dong F. Granular representation and granular computing with fuzzy sets Fuzzy Sets and Systems. 203: 17-32. DOI: 10.1016/J.Fss.2012.03.009 |
0.452 |
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2012 |
Roh S, Ahn T, Pedrycz W. Fuzzy linear regression based on Polynomial Neural Networks Expert Systems With Applications. 39: 8909-8928. DOI: 10.1016/J.Eswa.2012.02.016 |
0.478 |
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2012 |
Pedrycz W, Syed Ahmad SS. Evolutionary feature selection via structure retention Expert Systems With Applications. 39: 11801-11807. DOI: 10.1016/J.Eswa.2011.09.154 |
0.668 |
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2012 |
Liu X, Zhai K, Pedrycz W. An improved association rules mining method Expert Systems With Applications. 39: 1362-1374. DOI: 10.1016/J.Eswa.2011.08.018 |
0.329 |
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2012 |
Park H, Pedrycz W, Chung Y, Oh S. Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks Expert Systems With Applications. 39: 1021-1039. DOI: 10.1016/J.Eswa.2011.07.103 |
0.375 |
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2012 |
Oh S, Kim W, Pedrycz W. Design of optimized cascade fuzzy controller based on differential evolution: Simulation studies and practical insights Engineering Applications of Artificial Intelligence. 25: 520-532. DOI: 10.1016/J.Engappai.2012.01.002 |
0.407 |
|
2012 |
Wang X, Liu X, Pedrycz W, Zhu X, Hu G. Mining axiomatic fuzzy set association rules for classification problems European Journal of Operational Research. 218: 202-210. DOI: 10.1016/J.Ejor.2011.04.022 |
0.461 |
|
2012 |
Pedrycz W, Song M. A genetic reduction of feature space in the design of fuzzy models Applied Soft Computing. 12: 2801-2816. DOI: 10.1016/J.Asoc.2012.03.055 |
0.65 |
|
2012 |
Pedrycz W, Russo B, Succi G. Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity Applied Soft Computing Journal. 12: 1985-1995. DOI: 10.1016/J.Asoc.2012.02.004 |
0.409 |
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2012 |
Lee H, Kim E, Pedrycz W. A new selective neural network ensemble with negative correlation Applied Intelligence. 37: 488-498. DOI: 10.1007/S10489-012-0342-3 |
0.336 |
|
2012 |
Oh S, Park H, Kim W, Pedrycz W. A new approach to radial basis function-based polynomial neural networks: analysis and design Knowledge and Information Systems. 36: 121-151. DOI: 10.1007/S10115-012-0551-4 |
0.454 |
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2011 |
Długosz R, Kolasa M, Pedrycz W, Szulc M. Parallel programmable asynchronous neighborhood mechanism for Kohonen SOM implemented in CMOS technology. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 22: 2091-104. PMID 22049367 DOI: 10.1109/Tnn.2011.2169809 |
0.306 |
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2011 |
Kim I, Chu YY, Watada J, Wu JY, Pedrycz W. A DNA-based algorithm for minimizing decision rules: a rough sets approach. Ieee Transactions On Nanobioscience. 10: 139-51. PMID 22020105 DOI: 10.1109/Tnb.2011.2168535 |
0.406 |
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2011 |
Ramli AA, Watada J, Pedrycz W. An intelligent data analysis-base: evaluation of nuclear power plants output flow International Journal of Machine Learning and Computing. 176-184. DOI: 10.7763/Ijmlc.2011.V1.26 |
0.425 |
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2011 |
Pedrycz W. Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing International Journal of Cognitive Informatics and Natural Intelligence. 5: 44-60. DOI: 10.4018/Jcini.2011100104 |
0.391 |
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2011 |
Pedrycz W. The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing Journal of Information Processing Systems. 7: 397-412. DOI: 10.3745/Jips.2011.7.3.397 |
0.426 |
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2011 |
Minghu H, Chao W, Pedrycz W. The theoretical foundations of statistical learning theory based on fuzzy random samples in Sugeno measure space Transactions of the Institute of Measurement and Control. 34: 520-526. DOI: 10.1177/0142331211403796 |
0.372 |
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2011 |
Hu Q, Yu D, Pedrycz W, Chen D. Kernelized Fuzzy Rough Sets and Their Applications Ieee Transactions On Knowledge and Data Engineering. 23: 1649-1667. DOI: 10.1109/Tkde.2010.260 |
0.498 |
|
2011 |
Pedrycz W, Song M. Analytic Hierarchy Process (AHP) in Group Decision Making and its Optimization With an Allocation of Information Granularity Ieee Transactions On Fuzzy Systems. 19: 527-539. DOI: 10.1109/Tfuzz.2011.2116029 |
0.593 |
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2011 |
Mashinchi MH, Orgun MA, Mashinchi M, Pedrycz W. A Tabu–Harmony Search-Based Approach to Fuzzy Linear Regression Ieee Transactions On Fuzzy Systems. 19: 432-448. DOI: 10.1109/Tfuzz.2011.2106791 |
0.455 |
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2011 |
Ha M, Chen J, Pedrycz W, Sun L. Bounds on the rate of convergence of learning processes based on random sets and set‐valued probability Kybernetes. 40: 1459-1485. DOI: 10.1108/03684921111169486 |
0.34 |
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2011 |
Oh S, Jang H, Pedrycz W. Optimized fuzzy PD cascade controller: A comparative analysis and design Simulation Modelling Practice and Theory. 19: 181-195. DOI: 10.1016/J.Simpat.2010.06.004 |
0.333 |
|
2011 |
Pedrycz W. Information granules and their use in schemes of knowledge management Scientia Iranica. 18: 602-610. DOI: 10.1016/J.Scient.2011.04.013 |
0.34 |
|
2011 |
Qian Y, Liang J, Pedrycz W, Dang C. An efficient accelerator for attribute reduction from incomplete data in rough set framework Pattern Recognition. 44: 1658-1670. DOI: 10.1016/J.Patcog.2011.02.020 |
0.373 |
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2011 |
Zhou J, Pedrycz W, Miao D. Shadowed sets in the characterization of rough-fuzzy clustering Pattern Recognition. 44: 1738-1749. DOI: 10.1016/J.Patcog.2011.01.014 |
0.425 |
|
2011 |
Song M, Pedrycz W. From local neural networks to granular neural networks: A study in information granulation Neurocomputing. 74: 3931-3940. DOI: 10.1016/J.Neucom.2011.08.009 |
0.568 |
|
2011 |
Oh SK, Pedrycz W, Roh SB. Genetically optimized Hybrid Fuzzy Set-based Polynomial Neural Networks Journal of the Franklin Institute. 348: 415-425. DOI: 10.1016/J.Jfranklin.2010.11.005 |
0.447 |
|
2011 |
Aliev RA, Pedrycz W, Guirimov BG, Aliev RR, Ilhan U, Babagil M, Mammadli S. Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization Information Sciences. 181: 1591-1608. DOI: 10.1016/J.Ins.2010.12.014 |
0.51 |
|
2011 |
Roh SB, Oh SK, Pedrycz W. Design of fuzzy radial basis function-based polynomial neural networks Fuzzy Sets and Systems. 185: 15-37. DOI: 10.1016/J.Fss.2011.06.014 |
0.483 |
|
2011 |
Bandyopadhyay S, Saha S, Pedrycz W. Use of a fuzzy granulation–degranulation criterion for assessing cluster validity Fuzzy Sets and Systems. 170: 22-42. DOI: 10.1016/J.Fss.2010.11.015 |
0.401 |
|
2011 |
Oh S, Kim W, Pedrycz W, Park B. Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization Fuzzy Sets and Systems. 163: 54-77. DOI: 10.1016/J.Fss.2010.08.007 |
0.468 |
|
2011 |
Oh S, Jang H, Pedrycz W. A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization Expert Systems With Applications. 38: 11217-11229. DOI: 10.1016/J.Eswa.2011.02.169 |
0.376 |
|
2011 |
Hu Q, Zhang L, Zhang D, Pan W, An S, Pedrycz W. Measuring relevance between discrete and continuous features based on neighborhood mutual information Expert Systems With Applications. 38: 10737-10750. DOI: 10.1016/J.Eswa.2011.01.023 |
0.372 |
|
2011 |
Park H, Chung Y, Oh S, Pedrycz W, Kim H. Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet Engineering Applications of Artificial Intelligence. 24: 543-554. DOI: 10.1016/J.Engappai.2010.11.001 |
0.411 |
|
2011 |
Ramli AA, Watada J, Pedrycz W. Real-time fuzzy regression analysis: A convex hull approach European Journal of Operational Research. 210: 606-617. DOI: 10.1016/J.Ejor.2010.10.007 |
0.461 |
|
2011 |
Castillo O, Melin P, Pedrycz W. Design of interval type-2 fuzzy models through optimal granularity allocation Applied Soft Computing. 11: 5590-5601. DOI: 10.1016/J.Asoc.2011.04.005 |
0.482 |
|
2011 |
Han S, Park S, Pedrycz W. Conditional fuzzy clustering for blind channel equalization Applied Soft Computing. 11: 2777-2786. DOI: 10.1016/J.Asoc.2010.11.008 |
0.31 |
|
2011 |
Mashinchi MH, Orgun MA, Pedrycz W. Hybrid optimization with improved tabu search Applied Soft Computing. 11: 1993-2006. DOI: 10.1016/J.Asoc.2010.06.015 |
0.324 |
|
2011 |
Pedrycz W, Chen SC, Rubin SH, Lee G. Risk evaluation through decision-support architectures in threat assessment and countering terrorism Applied Soft Computing Journal. 11: 621-631. DOI: 10.1016/J.Asoc.2009.12.022 |
0.436 |
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2010 |
Dlugosz R, Talaska T, Pedrycz W, Wojtyna R. Realization of the conscience mechanism in CMOS implementation of winner-takes-all self-organizing neural networks. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 21: 961-71. PMID 20421180 DOI: 10.1109/Tnn.2010.2046497 |
0.315 |
|
2010 |
Chen L, Chen CL, Pedrycz W. A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 40: 1219-30. PMID 19963699 DOI: 10.1109/TSMCB.2009.2036443 |
0.316 |
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2010 |
Wang S, Watada J, Pedrycz W. Recourse-based facility-location problems in hybrid uncertain environment. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 40: 1176-87. PMID 19955039 DOI: 10.1109/TSMCB.2009.2035630 |
0.323 |
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2010 |
Pedrycz W. Conditional fuzzy clustering in the design of radial basis function neural networks. Ieee Transactions On Neural Networks. 9: 601-12. PMID 18252484 DOI: 10.1109/72.701174 |
0.385 |
|
2010 |
Pedrycz W. Granular Computing and Human-Centricity in Computational Intelligence International Journal of Software Science and Computational Intelligence. 2: 16-31. DOI: 10.4018/Jssci.2010100102 |
0.464 |
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2010 |
Sadeghi N, Fayek AR, Pedrycz W. Fuzzy Monte Carlo simulation and risk assessment in construction Computer-Aided Civil and Infrastructure Engineering. 25: 238-252. DOI: 10.1111/J.1467-8667.2009.00632.X |
0.391 |
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2010 |
Roh S, Ahn T, Pedrycz W. The refinement of models with the aid of the fuzzy k-nearest neighbors approach Ieee Transactions On Instrumentation and Measurement. 59: 604-615. DOI: 10.1109/Tim.2009.2025070 |
0.395 |
|
2010 |
Pedrycz W, Loia V, Senatore S. Fuzzy Clustering With Viewpoints Ieee Transactions On Fuzzy Systems. 18: 274-284. DOI: 10.1109/Tfuzz.2010.2040479 |
0.462 |
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2010 |
Pizzi NJ, Demko A, Pedrycz W. Classification using an adaptive fuzzy network Annual Conference of the North American Fuzzy Information Processing Society - Nafips. DOI: 10.1109/NAFIPS.2010.5548179 |
0.329 |
|
2010 |
Pedrycz W. The development of granular metastructures and their use in a multifaceted representation of data and models Kybernetes. 39: 1184-1200. DOI: 10.1108/03684921011062773 |
0.45 |
|
2010 |
Pedrycz W. Hierarchical Architectures of Fuzzy Models: From Type-1 fuzzy sets to Information Granules of Higher Type International Journal of Computational Intelligence Systems. 3: 202-214. DOI: 10.1080/18756891.2010.9727691 |
0.487 |
|
2010 |
Cheng S, Dong R, Pedrycz W. A framework of fuzzy hybrid systems for modelling and control International Journal of General Systems. 39: 165-176. DOI: 10.1080/03081070903427358 |
0.444 |
|
2010 |
Pedrycz W, Bargiela A. Fuzzy clustering with semantically distinct families of variables: Descriptive and predictive aspects Pattern Recognition Letters. 31: 1952-1958. DOI: 10.1016/J.Patrec.2010.06.018 |
0.376 |
|
2010 |
Mitra S, Pedrycz W, Barman B. Shadowed c-means: Integrating fuzzy and rough clustering Pattern Recognition. 43: 1282-1291. DOI: 10.1016/J.Patcog.2009.09.029 |
0.388 |
|
2010 |
Roh S, Joo S, Pedrycz W, Oh S. The development of fuzzy radial basis function neural networks based on the concept of information ambiguity Neurocomputing. 73: 2464-2477. DOI: 10.1016/J.Neucom.2010.05.006 |
0.495 |
|
2010 |
Roh S, Oh S, Pedrycz W. A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering Knowledge-Based Systems. 23: 202-219. DOI: 10.1016/J.Knosys.2009.12.002 |
0.476 |
|
2010 |
Pizzi NJ, Pedrycz W. Aggregating multiple classification results using fuzzy integration and stochastic feature selection International Journal of Approximate Reasoning. 51: 883-894. DOI: 10.1016/J.Ijar.2010.05.003 |
0.362 |
|
2010 |
Hu Q, Zhang L, Chen D, Pedrycz W, Yu D. Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications International Journal of Approximate Reasoning. 51: 453-471. DOI: 10.1016/J.Ijar.2010.01.004 |
0.521 |
|
2010 |
Stach W, Kurgan L, Pedrycz W. A divide and conquer method for learning large Fuzzy Cognitive Maps Fuzzy Sets and Systems. 161: 2515-2532. DOI: 10.1016/J.Fss.2010.04.008 |
0.428 |
|
2010 |
Castillo O, Melin P, Pedrycz W, Kacpzryk J. Preface to the special section on new trends on pattern recognition with fuzzy models Fuzzy Sets and Systems. 161: 1-2. DOI: 10.1016/J.Fss.2009.10.023 |
0.384 |
|
2010 |
Graves D, Pedrycz W. Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study Fuzzy Sets and Systems. 161: 522-543. DOI: 10.1016/J.Fss.2009.10.021 |
0.435 |
|
2010 |
Roh S, Ahn T, Pedrycz W. The design methodology of radial basis function neural networks based on fuzzy K-nearest neighbors approach Fuzzy Sets and Systems. 161: 1803-1822. DOI: 10.1016/J.Fss.2009.10.014 |
0.461 |
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