Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem

In this paper we propose a new model of particle swarm optimization called two-step PSO. The basic idea is to split the heuristic search performed by particles into two stages. We have studied the performance of this new algorithm for the feature selection problem by using the reduct concept of the rough set theory. Experimental results obtained show that the two-step approach improves over the PSO model in calculating reducts, with the same computational cost.