Mutual information estimation for filter based feature selection using particle swarm optimization

Abstract

Feature selection is a pre-processing step in classification, which selects a small set of important features to improve the classification performance and efficiency. Mutual information is very popular in feature selection because it is able to detect non-linear relationship between features. However the existing mutual information approaches only consider two-way interaction between features. In addition, in most methods, mutual information is calculated by a counting approach, which may lead to an inaccurate results. This paper proposes a filter feature selection algorithm based on particle swarm optimization (PSO) named PSOMIE, which employs a novel fitness function using nearest neighbor mutual information estimation (NNE) to measure the quality of a feature set. PSOMIE is compared with using all features and two traditional feature selection approaches. The experiment results show that the mutual information estimation successfully guides PSO to search for a small number of features while maintaining or improving the classification performance over using all features and the traditional feature selection methods. In addition, PSOMIE provides a strong consistency between training and test results, which may be used to avoid overfitting problem.