A new mutual information based measure for feature selection

Abstract

In this paper, we discuss the problem of feature selection and the importance of using mutual information in evaluating the discrimination ability of feature subsets between class labels. Because of the difficulties associated with estimating the exact value of mutual information, we propose a new evaluation measure that is based on the information gain and takes into consideration the interaction between features. The proposed measure is integrated into a robust feature selection scheme and compared with the well-known mutual information feature selection (MIFS) algorithm using the problems of texture classification, speech segment classification and speaker identification.