| MGRS: A multi-granulation rough set Y Qian, J Liang, Y Yao, C Dang Information sciences 180 (6), 949-970, 2010 | 867 | 2010 |
| Positive approximation: an accelerator for attribute reduction in rough set theory Y Qian, J Liang, W Pedrycz, C Dang Artificial intelligence 174 (9-10), 597-618, 2010 | 800 | 2010 |
| Incomplete multigranulation rough set Y Qian, J Liang, C Dang IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2009 | 446 | 2009 |
| A new method for measuring uncertainty and fuzziness in rough set theory J Liang, KS Chin, C Dang, RCM Yam International Journal of General Systems 31 (4), 331-342, 2002 | 424 | 2002 |
| A group incremental approach to feature selection applying rough set technique J Liang, F Wang, C Dang, Y Qian IEEE transactions on knowledge and data engineering 26 (2), 294-308, 2012 | 347 | 2012 |
| Stability analysis of positive switched linear systems with delays X Liu, C Dang IEEE Transactions on Automatic Control 56 (7), 1684-1690, 2011 | 318 | 2011 |
| Knowledge structure, knowledge granulation and knowledge distance in a knowledge base Y Qian, J Liang, C Dang International Journal of Approximate Reasoning 50 (1), 174-188, 2009 | 240 | 2009 |
| Set-valued ordered information systems Y Qian, C Dang, J Liang, D Tang Information Sciences 179 (16), 2809-2832, 2009 | 227 | 2009 |
| Interval ordered information systems Y Qian, J Liang, C Dang Computers & Mathematics with Applications 56 (8), 1994-2009, 2008 | 227 | 2008 |
| An efficient rough feature selection algorithm with a multi-granulation view J Liang, F Wang, C Dang, Y Qian International journal of approximate reasoning 53 (6), 912-926, 2012 | 222 | 2012 |
| An efficient accelerator for attribute reduction from incomplete data in rough set framework Y Qian, J Liang, W Pedrycz, C Dang Pattern Recognition 44 (8), 1658-1670, 2011 | 222 | 2011 |
| Information granularity in fuzzy binary GrC model Y Qian, J Liang, ZW Wei-zhi, C Dang IEEE Transactions on Fuzzy Systems 19 (2), 253-264, 2010 | 185 | 2010 |
| An evolutionary algorithm for global optimization based on level-set evolution and latin squares Y Wang, C Dang IEEE Transactions on Evolutionary Computation 11 (5), 579-595, 2007 | 176 | 2007 |
| A dissimilarity measure for the k-modes clustering algorithm F Cao, J Liang, D Li, L Bai, C Dang Knowledge-Based Systems 26, 120-127, 2012 | 165 | 2012 |
| An aftertreatment technique for improving the accuracy of Adomian's decomposition method YC Jiao, Y Yamamoto, C Dang, Y Hao Computers & Mathematics with Applications 43 (6-7), 783-798, 2002 | 161 | 2002 |
| Fuzzy-rough feature selection accelerator Y Qian, Q Wang, H Cheng, J Liang, C Dang Fuzzy Sets and Systems 258, 61-78, 2015 | 160 | 2015 |
| Determining the number of clusters using information entropy for mixed data J Liang, X Zhao, D Li, F Cao, C Dang Pattern Recognition 45 (6), 2251-2265, 2012 | 160 | 2012 |
| Local rough set: a solution to rough data analysis in big data Y Qian, X Liang, Q Wang, J Liang, B Liu, A Skowron, Y Yao, J Ma, C Dang International Journal of Approximate Reasoning 97, 38-63, 2018 | 145 | 2018 |
| Measures for evaluating the decision performance of a decision table in rough set theory Y Qian, J Liang, D Li, H Zhang, C Dang Information Sciences 178 (1), 181-202, 2008 | 133 | 2008 |
| The -Means-Type Algorithms Versus Imbalanced Data Distributions J Liang, L Bai, C Dang, F Cao IEEE Transactions on Fuzzy Systems 20 (4), 728-745, 2012 | 131 | 2012 |
