A Diversity Based Competitive Multi-objective PSO for Feature Selection

摘要

Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection has attracted increasing attention of researchers recently. However, in the existing methods, quick convergence usually degrades the diversity of the population, especially when many irrelevant and redundant features involved in them. To this end, a diversity based competitive multi-objective particle swarm optimization for feature selection problem (named D-CMOPSO) is proposed. In D-CMOPSO, a diversified competition based learning mechanism is proposed to improve the quality of found feature subset, which consists of three parts: exemplar particle construction, pairwise competition, and diversified learning strategy. The proposed competition mechanism utilizes the above three parts to boost the diversity in the following generations. Moreover, in order to guide the initial population to evolve the promising area, a maximal information coefficient based initialization strategy is also suggested. The experimental results demonstrate that the proposed D-CMOPSO is competitive for feature selection problem.