A new mutual information based measure for feature selection

Authors:
Ahmed Al-Ani;Mohamed Deriche;Jalel Chebil
Affiliations:
Signal Processing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia. E-mail: {a.alani,m.deriche,j.chebil}@qut.edu.au;Signal Processing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia. E-mail: {a.alani,m.deriche,j.chebil}@qut.edu.au;Signal Processing Research Centre, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia. E-mail: {a.alani,m.deriche,j.chebil}@qut.edu.au
Venue:
Intelligent Data Analysis
Year:
2003

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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.