Identifying core sets of discriminatory features using particle swarm optimization

Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. New optimization techniques emerging in areas such as Computational Intelligence have been investigated in the context of feature selection. Here, we propose an original two-phase feature selection method that uses particle swarm optimization (PSO), a biologically inspired optimization technique, which forms an initial core set of discriminatory features from the original feature space. This core set is then successively expanded by searching for additional discriminatory features. The performance of the proposed PSO feature selection method is evaluated using a nearest neighbor classifier. The design of the optimally reduced feature space is investigated in a parametric setting by varying the size of the core feature set and the training set. Numerical experiments, using data from the Machine Learning Repository, show that a substantial reduction of the feature space is accomplished. A thorough comparative analysis of results reported in the literature also reveals improvement in classification accuracy.