Two Step Swarm Intelligence to Solve the Feature Selection Problem

Yudel Gómez (Universidad Central de Las Villas, Cuba)

Rafael Bello (Universidad Central de Las Villas, Cuba)

Amilkar Puris (Universidad Central de Las Villas, Cuba)

María M. García (Universidad Central de Las Villas, Cuba)

Ann Nowe (Vrije Universiteit Brussel, Belgium)

Abstract: In this paper we propose a new approach to Swarm Intelligence called Two-Step Swarm Intelligence. The basic idea is to split the heuristic search performed by agents into two stages. In the first step the agents build partial solutions which, are used as initial states in the second step. We have studied the performance of this new approach for the Feature Selection Problem by using Ant Colony Optimization and Particle Swarm Optimization. The feature selection is based on the reduct concept of the Rough Set Theory. Experimental results obtained show that Two-step approach improves the performance of ACO and PSO metaheuristics when calculating reducts in terms of computation time cost and the quality of reducts.

Keywords: Ant Colony Optimization, Feature Selection Problem, Particle Swarm Optimization, Rough Set Theory, Swarm Intelligence, Two-Step Swarm Intelligence

Categories: I.2.6, I.2.8, I.5.1