JUCS - Journal of Universal Computer Science 14(15): 2582-2596, doi: 10.3217/jucs-014-15-2582
Two Step Swarm Intelligence to Solve the Feature Selection Problem
Yudel Gómez‡, Rafael Bello‡, Amilkar Puris‡, María M. García‡, Ann Nowe§‡ Universidad Central de Las Villas, Santa Clara, Cuba§ Vrije Universiteit Brussel, Brussel, Belgium
Corresponding author: Yudel Gómez ( ygomezd@uclv.edu.cu ) © Yudel Gómez, Rafael Bello, Amilkar Puris, María García, Ann Nowe. This article is freely available under the J.UCS Open Content License. Citation:
Gómez Y, Bello R, Puris A, García MM, Nowe A (2008) Two Step Swarm Intelligence to Solve the Feature Selection Problem. JUCS - Journal of Universal Computer Science 14(15): 2582-2596. https://doi.org/10.3217/jucs-014-15-2582 |  |
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
Feature Selection Problem, Swarm Intelligence, Ant Colony Optimization, Particle Swarm Optimization, Rough Set Theory, Two-Step Swarm Intelligence