An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system

Feature selection (FS) is a most important step which can affect the performance of a pattern recognition system. This paper proposes a novel feature selection method based on ant colony optimization (ACO). ACO algorithm is inspired of ant’s social behavior in their search for the shortest paths to food sources. Most common techniques for ACO-based feature selection use the priori information of features. However, in the proposed algorithm classifier performance and the length of the selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset in terms of shortest feature length and the best performance of classifier. The experimental results on face recognition system using ORL database show that the proposed approach is easily implemented and without any priori information of features, its total performance is better than that of GA-based and other ACO-based feature selection methods.