Optimal Scale Selection and Rule Acquisition in Inconsistent Generalized Decision Multi-scale Ordered Information Systems

Granular computing imitates human being's thinking.It shows great promise as a new way for data mining and know-ledge discovery in the context of big data.To solve the problem of knowledge acquisition in inconsistent generalized decision multi-scale ordered information systems,by employing evidence theory,the optimal scale combination and rule extraction in inconsistent generalized decision multi-scale ordered information systems are studied.Dominance relations are first introduced into decision multi-scale information systems,and some basic concepts in decision multi-scale ordered information systems are introduced.With reference to the notion of scale combinations in inconsistent generalized decision multi-scale ordered information systems,representations of information granules as well as lower and upper approximations of sets under different scale combinations are presented and their relationships are examined.With different scales of decisions,several types of optimal scale combinations in inconsistent generalized decision multi-scale ordered information systems are further defined and their relationships are clarified.Finally,a method of discernibility matrix attribute reduction and rule acquisition based on generalized dominance decision functions are explored.