[1]ZHANG Xiaohe,MI Jusheng,LI Meizheng.Attribute reduction and rule fusion in granular consistent formal decision contexts[J].CAAI Transactions on Intelligent Systems,2019,14(6):1138-1143.[doi:10.11992/tis.201905050]
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Attribute reduction and rule fusion in granular consistent formal decision contexts

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1138-1143 Column: 学术论文—机器感知与模式识别 Public date: 2019-11-05
Title:
Attribute reduction and rule fusion in granular consistent formal decision contexts
Author(s):
ZHANG Xiaohe1;  MI Jusheng1;  LI Meizheng2
1. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China;
2. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
Keywords:
attribute reduction; decision rules; formal context; discernibility matrix; inclusions; extracting rules; granular computing; concept lattice
CLC:
O236;TP18
DOI:
10.11992/tis.201905050
Abstract:
Attribute reduction and rule acquisition based on formal decision contexts can acquire knowledge more conveniently and effectively; thus, rule acquisition and attribute reduction are two key research directions of the theory of formal concept analysis (FCA). This study investigates attribute reduction and rule acquisition based on an equivalence relation in formal granular consistent decision contexts. In this paper, the granular consistent set and granular reduction are defined, and the judgment theory of the granular consistent set is given, and by combination with the Boolean method, the granular reduction is formulated. Finally, using the inclusion degree of set-valued vectors, optimistic and pessimistic rule fusion methods in formal decision contexts are proposed.
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