Computer Science ›› 2017, Vol. 44 ›› Issue (5): 199-205.doi: 10.11896/j.issn.1002-137X.2017.05.036

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Granular Structure Reduction Approach to Multigranulation Decision-theoretic Rough Sets

SANG Yan-li and QIAN Yu-hua   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Multigranulation decision-theoretic rough set method (MG-DTRS) is a generalization of multigranulation rough set model through combining the decision-theoretic rough sets theory and the multigranulation idea,which is a data modeling method on decision-theoretic rough sets in the context of multiple granular spaces.Further,based on Baye-sian decision theory,we made a concrete analysis about probability fusion relations used optimistic or pessimistic fusion strategies on multiple granular spaces,also,the approximate representation of the maximum conditional probability rough sets and the minimum conditional probability rough sets were proposed respectively.And then the optimistic MG-DTRS model and the pessimistic MG-DTRS model were constructed.Furthermore,a concept of the approximate distribution reduction was introduced to MG-DTRS model,and the granular structure selection problem under multiple granular spaces was investigated.Based on the multiple granular approximate distribution quality proposed in this mo-del,the important measure of a granular structure was defined,and an α-lower approximate distribution reduction algorithm to obtain a granular structure reduction was designed under optimistic or pessimistic fusion strategies respectively.Finally,an example was employed for verifying the validity of the proposed algorithm.

Key words: Multigranulation decision-theoretic rough sets,Bayesian decision theory,α-lower approximate distribution reduction,Granular structure reduction,Approximate distribution quality

[1] PAWLAK Z.Rough Set[J].International Journal of Computer and Information Science,1982,1:341-356.
[2] PAWLAK Z,Skowron A.Rudiments of rough sets[J].Information Sciences,2007,7:3-27.
[3] 梁吉业,李德玉.信息系统中的不确定性与知识获取[M].北京:科学出版社,2005:42-67.
[4] ZIARKO W.Variable precision rough set model[J].Journal of Computer System Sciences,1993,46(1):39-59.
[5] SLEZAK D,ZIARKO W.The investigation of the Bayesianrough set model[J].International Journal of Approximate Reasoning,2005,40:81-91.
[6] YAO Y Y,WONG S K M.A decision theoretic framework for approximating concepts[J].International Journal of Man-machine Studies,1992,7:793-809.
[7] YAO Y Y.Three-way decision with probabilistic rough sets[J].Information Sciences,2010,0:341-353.
[8] LIN T Y.Granular computing on binary relations Ⅱ:Rough set representations and belief functions[M]∥Rough Sets an Knowledge Discovery.1998:122-140.
[9] YAO Y Y,SHE Y H.Rough set models in multigranulationspaces[J].Information Sciences,2016,327:40-56.
[10] QIAN Y H,LIANG J Y,YAO Y Y,et al.MGRS:a multi-gra-nulation rough set[J].Information Sciences,2010,0:949-970.
[11] QIAN Y H,LI S Y,LIANG J Y,et al.Pessimistic rough set based decisions:A multigranulation fusion strategy[J].Information Sciences,2014,4(20):196-210.
[12] QIAN Y H,ZHANG H,SANG Y L,et al.Multigranulation decision-theoretic rough sets[J].International Journal Approximate Reason,2014,55:225-237.
[13] SANG Y L,QIAN Y H.A Granular Space Reduction Approach to Pessimistic Multi—Granulation Rough Sets[J].Pattern Re-cognition and Artificial Intelligence,2012,25(3):361-366.(in Chinese) 桑妍丽,钱宇华.一种悲观多粒度粗糙集中的粒度约简算法[J].模式识别与人工智能,2012,25(3):361-366.
[14] ZHANG M,TANG Z M,XU W Y,et al.Variable Multi-granulation Rough Set Model[J].Pattern Recognition and Artificial Intelligence,2012,25(4):709-720.(in Chinese) 张明,唐振民,徐维艳,等.可变多粒度粗糙集模型[J].模式识别与人工智能,2012,25(4):709-720.
[15] MENG H L,MA Y Y,XU J C.Granularity Reduction of Variable Precision Pessimistic Multi-granulation Rough Set Based on Granularity Entropy of Lower Approximate Distribution[J].Computer Science,2016,3(2):83-86.(in Chinese) 孟慧丽,马媛媛,徐久成.基于下近似分布粒度熵的变精度悲观多粒度粗糙集粒度约简[J].计算机科学,2016,43(2):83-86.
[16] YANG X B,QI Y,YU H L,et al.Updating multigranulation rough approximations with increasing of granular structures[J].Knowledge-Based Systems,2014,64:59-69
[17] ZHAI Y J,ZHANG H.Reduction of Variable Precision Multi-granulation Rough Sets[J].Journal of Jinling Institute of Technology,2013,29(4):1-8.(in Chinese) 翟永建,张宏.变精度多粒度粗糙集的约简研究[J].金陵科技学院学报,2013,29(4):1-8.
[18] ZHANG W X,MI J S,WU W Z.Knowledge Reductions in Inconsistent Information Systems[J].Chinese Journal of Compu-ters,2003,26(1):12-18.(in Chinese) 张文修,米据生,吴伟志.不协调目标信息系统的知识约简[J].计算机学报,2003,26(1):12-18.

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