Attribute Reduction of Formal Contexts Based on Decision Rules
LI Tongjun, XU Yingcong, WU Weizhi, GU Shenming
School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan 316022 Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022
Abstract：By using the binary relation between objects and attributes, one pair of lower and upper rough fuzzy approximation operators is defined in formal contexts, properties of the approximation operators are explored, and the relationship between the defined approximation operators and the existing rough approximation operators is revealed. By using the defined approximation operators, two types of decision rules can be extracted, i.e., the certainty rules and the possibility rules. Subsequently, with respect to two types of decision rules, notions of lower and upper approximation reductions are proposed. For the upper approximation reduction, some necessary and sufficient conditions for reducible attributes and consistent subsets of attributes are obtained. An approach for attribute reduction is presented, and some illustration examples are given to show its reliability.
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2017, Vol. 30 
