Attribute Reduction Alogrithm Based on Information Entropy and Its Application

Attribute reduction is one of the important issues of rough set.It can remove superfluous knowledge from decision system which the ability of classification is preserved,and improve efficiency of decision system.This paper expounds the basic conceptions of rough set theory and information entropy,and an algorithm of attribute reduction based on rough set and information entropy is put forward.The algorithm from the point of view of relative core,make information entropy and conditional entropy and the important degree of attributes together to use.This algorithm optimizes the structure of the original algorithm and speeds up the speed of attribute reduction.The proposed algorithm is validated by experiment through using CTR and Wine data set.The experimental results show that the algorithm can obtain the optimal attribute reduction of decision system,and speed up the speed.