An efficient ant colony optimization approach to attribute reduction in rough set theory

Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we introduce a new approach based on ant colony optimization (ACO) for attribute reduction. To verify the proposed algorithm, numerical experiments are carried out on thirteen small or medium-sized datasets and three gene expression datasets. The results demonstrate that this algorithm can provide competitive solutions efficiently.