Efficient ant colony optimization for image feature selection

Feature selection (FS) is an important task which can significantly affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, existing ACO-based feature selection methods need to traverse a complete graph with O(n2) edges. However, we propose a novel algorithm in which the artificial ants traverse on a directed graph with only O(2n) arcs. The algorithm incorporates the classification performance and feature set size into the heuristic guidance, and selects a feature set with small size and high classification accuracy. We perform extensive experiments on two large image databases and 15 non-image datasets to show that our proposed algorithm can obtain higher processing speed as well as better classification accuracy using a smaller feature set than other existing methods.

► A feature selection algorithm based on ant colony optimization is presented. ► The algorithm can obtain higher processing speed than other existing methods. ► The algorithm can select a smaller feature set than other existing methods. ► Higher quality classification results are obtained using such smaller feature set. ► The advantages of the algorithm are proved empirically.