Comparative Study on Optimal Granularities in Inconsistent Multi-granular Labeled Decision Systems
WU Weizhi, CHEN Chaojun, LI Tongjun, XU Youhong
School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan 316022 Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhoushan 316022
Abstract To study knowledge representation and knowledge acquisition in inconsistent decision systems with multi-granular labels, the concept of multi-granular labeled information systems is firstly introduced. Indiscernibility relations on the universe of discourse in a multi-granular labeled information system are defined. Representations of equivalence classes with different levels of granulation as well as their relationships are also explored. Lower and upper approximations of sets with different levels of granulation are further defined and their properties are presented. Finally, concepts of eight types of consistence and optimal granularity with various meanings in inconsistent multi-granular labeled decision systems are proposed and their relationships are examined.
Fund:Supported by National Natural Science Foundation of China (No.41631179,61573321,61272021,61202206), Natural Science Foundation of Zhejiang Province (No.LY14F030001), Open Foundation from Marine Sciences in the Most Important Subjects of Zhejiang Province(No.20160102)
About author:: (WU Weizhi(Corresponding author), born in 1964, Ph.D., professor. His research interests include rough sets, granular computing, data mining and artificial intelligence.)(CHEN Chaojun, born in 1992, master student. Her research interests include rough sets and data mining.)(LI Tongjun, born in 1966, Ph.D., professor. His research interests include rough sets, granular computing, concept lattice and data mining.)(XU Youhong, born in 1969, master, associate professor. Her research interests include rough sets and granular computing.)
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2016, Vol. 29 
