期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
卷 40, 期 2, 页码 420-431出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCA.2009.2035436
关键词
Attribute reduction; granular computing; information systems (ISs); rough set
资金
- National Natural Science Foundation of China [60773133, 70471003, 60573074]
- High Technology Research and Development Program of China [2007AA01Z165]
- National Key Basic Research and Development Program of China (973) [2007CB311002]
- Government of Hong Kong SAR [CERG: CityU 101005]
The original rough-set model is primarily concerned with the approximations of sets described by a single equivalence relation on a given universe. With granular computing point of view, the classical rough-set theory is based on a single granulation. This correspondence paper first extends the rough-set model based on a tolerance relation to an incomplete rough-setmodel based on multigranulations, where set approximations are defined through using multiple tolerance relations on the universe. Then, several elementary measures are proposed for this rough-set framework, and a concept of approximation reduct is introduced to characterize the smallest attribute subset that preserves the lower approximation and upper approximation of all decision classes in this rough-set model. Finally, several key algorithms are designed for finding an approximation reduct.
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