| Granular fuzzy rule-based models: A study in a comprehensive evaluation and construction of fuzzy models X Hu, W Pedrycz, X Wang IEEE Transactions on Fuzzy Systems 25 (5), 1342-1355, 2016 | 79 | 2016 |
| Fuzzy classifiers with information granules in feature space and logic-based computing X Hu, W Pedrycz, X Wang Pattern Recognition 80, 156-167, 2018 | 55 | 2018 |
| Fuzzy rule-based models with interactive rules and their granular generalization X Hu, W Pedrycz, O Castillo, P Melin Fuzzy Sets and Systems 307, 1-28, 2017 | 40 | 2017 |
| Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation X Hu, Y Shen, W Pedrycz, Y Li, G Wu IEEE Transactions on Cybernetics 52 (7), 6420 - 6433, 2021 | 33 | 2021 |
| Identification of fuzzy rule-based models with collaborative fuzzy clustering X Hu, Y Shen, W Pedrycz, X Wang, A Gacek, B Liu IEEE transactions on cybernetics 52 (7), 6406-6419, 2021 | 29 | 2021 |
| Optimal allocation of information granularity in system modeling through the maximization of information specificity: A development of granular input space X Hu, W Pedrycz, X Wang Applied Soft Computing 42, 410-422, 2016 | 29 | 2016 |
| From fuzzy rule-based models to their granular generalizations X Hu, W Pedrycz, X Wang Knowledge-Based Systems 124, 133-143, 2017 | 27 | 2017 |
| Data reconstruction with information granules: An augmented method of fuzzy clustering X Hu, W Pedrycz, G Wu, X Wang Applied Soft Computing 55, 523-532, 2017 | 25 | 2017 |
| Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics G Jin, Q Wang, C Zhu, Y Feng, J Huang, X Hu Applied Soft Computing 97, 106730, 2020 | 22 | 2020 |
| Random ensemble of fuzzy rule-based models X Hu, W Pedrycz, X Wang Knowledge-Based Systems 181, 104768, 2019 | 22 | 2019 |
| Information granule-based classifier: A development of granular imputation of missing data X Hu, W Pedrycz, K Wu, Y Shen Knowledge-Based Systems 214, 106737, 2021 | 20 | 2021 |
| Comparative analysis of logic operators: a perspective of statistical testing and granular computing X Hu, W Pedrycz, X Wang International Journal of Approximate Reasoning 66, 73-90, 2015 | 20 | 2015 |
| Development of granular models through the design of a granular output spaces X Hu, W Pedrycz, X Wang Knowledge-Based Systems 134, 159-171, 2017 | 19 | 2017 |
| Configuring differential evolution adaptively via path search in a directed acyclic graph for data clustering G Wu, W Peng, X Hu, R Wang, H Chen Swarm and Evolutionary Computation 55, 100690, 2020 | 15 | 2020 |
| Multi-view fuzzy classification with subspace clustering and information granules X Hu, X Liu, W Pedrycz, Q Liao, Y Shen, Y Li, S Wang IEEE Transactions on Knowledge and Data Engineering 35 (11), 11642-11655, 2022 | 13 | 2022 |
| Fuzzy rule-based models with randomized development mechanisms X Hu, W Pedrycz, D Wang Fuzzy Sets and Systems 361, 71-87, 2019 | 13 | 2019 |
| Multi-granulation-based optimal scale selection in multi-scale information systems H Wang, W Li, T Zhan, K Yuan, X Hu Computers & Electrical Engineering 92, 107107, 2021 | 7 | 2021 |
| An Efficient Federated Multi-view Fuzzy C-Means Clustering Method X Hu, J Qin, Y Shen, W Pedrycz, X Liu, J Liu IEEE Transactions on Fuzzy Systems, 2023 | 6 | 2023 |
| Multivariable fuzzy rule-based models and their granular generalization: A visual interpretable framework Y Li, X Hu, W Pedrycz, F Yang, Z Liu Applied Soft Computing 134, 109958, 2023 | 4 | 2023 |
| Fuzzy Rule-Based Models: A Design with Prototype Relocation and Granular Generalization Y Li, C Chen, X Hu, J Qin, Y Ma Information Sciences 562, 155-179, 2021 | 3 | 2021 |
Witold Pedrycz在 ualberta.ca 的电子邮件经过验证
