Two Step Swarm Intelligence to Solve the Feature Selection Problem
Дата
Авторы
Gómez,Yudel
Bello,Rafael
Puris,Amilkar
García,María
Nowe,Ann
Journal Title
Journal ISSN
Volume Title
Издатель
Journal of Universal Computer Science
Аннотация
Описание
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.
Ключевые слова
Feature Selection Problem , Swarm Intelligence , Ant Colony Optimization , Particle Swarm Optimization , Rough Set Theory , Two-Step Swarm Intelligence