An improved feature selection algorithm with conditional mutual information for classification problems

Abstract

The purpose of the feature selection is to eliminate insignificant features from entire dataset and simultaneously to keep the class discriminatory information for classification problems. Many feature selection algorithms have been proposed to measure the relevance and redundancy of the features and class variables. In this paper, we proposed an improved feature selection algorithm based on maximum relevance and minimum redundancy criterion. The relevance of a feature to the class variables are evaluated with mutual information and conditional mutual information is used to calculate the redundancy between the selected and the candidate features to each class variable. The experimental result is tested with five benchmarked datasets available from UCI Machine Learning Repository. The results shows the proposed algorithm is considered quite well when compared with some existing algorithms.