Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Feature selection (FS), which aims to select informative feature subsets and improve classification performance, is a crucial data-mining technique. Recently, swarm intelligence has attracted considerable attention and has been successfully applied to FS. Ant colony optimization (ACO), a swarm intelligence algorithm, has shown great potential in FS owing to its graphical representation and search ability. However, designing an effective ACO-based approach for FS is challenging because of issues originating from feature interactions and premature convergence problems. In this study, a novel ACO is proposed that incorporates symmetric uncertainty (SU). By constructing a probabilistic sequence-based graphical representation, the proposed algorithm significantly outperformed six other algorithms on 16 problems in terms of the classification error rate. This study also considers an extensive investigation of the contribution of the two components, namely, probabilistic sequence and SU. The experimental results indicated that these components significantly improved the performance of the ACO-based approach.