Model Of Intuitionistic Fuzzy Information System And Its Attribute Reduction

Rough set theory introduced by Pawlak can deal well with imprecise,vague and uncertain information One of the key issues of preprocessing techniques is knowledge reduc-tion.As is well-known,in knowledge base,attributes of describing knowledge are not equally important,and even some of attributes are redundant.On the one hand,they can cause the waste of resources;on the other hand,informations are so multifarious that people don' t make right decision.Knowledge reduction is keep the same classification ability of the knowledge base,and it can delete the irrelevant or unimportant attributes,so that the knowledge base become more simplified,which are the person would expect.The main research contents of this paper are as follows:(1)The concepts of mutual information and intuitionistic fuzzy rough sets are combined.This paper proposes a heuristic algorithm based on mutual information gain ratio in intuition-istic fuzzy rouge set.In the intuitionistic fuzzy environment,the paper also gives the fuzzy decision table based attribute importance measurement method,and the measurement method not only considers the attribute domain size but also considers the value of the distribution.(2)In incomplete intuitionistic fuzzy information system,this paper puts forward a new improved model.Firstly,by introducing a kind of expansion based on the similarity of the tolerance relation,we study the rough set theory from the perspective of multi granularity.We combined the incomplete intuitionistic fuzzy information systems with multi granulation rough set,and propose a new multi granulation rough set model,the incomplete intuitionistic fuzzy information system of multi granularity rough set model.This model gives the definitions of the concepts of upper approximation,lower approximation and so on.Some relevant properties and theorems are discussed and proved.Examples analyze explains the advantage of the present model in the end.