(San Y L, Qian Y H. A granular space reduction approach to pessimistic multi-granulation rough set[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(3): 361-366.)
[6]
Zhang M, Xu W Y, Yang X B, et al. Incomplete variable multigranulation rough sets decision[J]. Applied Mathematics & Information Sciences, 2014, 8(3): 1159-1166.
[7]
Xu W H, Sun W X, Zhang X Y, et al. Multiple granulation rough set approach to ordered information systems[J]. Int J of General Systemsl, 2012, 41(5): 475-501.
[8]
Yang X B, Song X N, Dou H L, et al. Multi-granulation rough set: From crisp to fuzzy case[J]. Annals of Fuzzy Mathematics and Informatics, 2011, 1(1): 55-70.
[9]
Lin G P, Qian Y H, Li J J. NMGRS: Neighborhood-based multigranulation rough sets[J]. Int J of Approximate Reasoning, 2012, 53(7): 1080-1093.
[10]
Qian Y H , Zhang H, Sang Y L, et al. Multigranulation decision-theoretic rough sets[J]. Int J of Approximate Reasoning, 2014, 55(1): 225-237.
(Zhang M, Tang Z M, Xu W Y, et al. Variable multigranulation rough set model[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(4): 709-720.)
[13]
Wu W Z, Leung Y. Theory and applications of granular labelled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181(18): 3878-3897.
[14]
She Y H, He X L. On the structure of the multigranulation rough set model[J]. Knowledge-Based Systems, 2012, 36(1): 81-92.
(Tian J, Zhang P Z,Wang K L, et al. The integraiong model of expert’s opinion based on Delphi method[J]. Systems Engineering - Theory & Practice, 2004, 24(1): 55-62.)
