Feature Selection Based on Ant Colony Optimization and Rough Set Theory

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. Rough set theory offers a viable approach for feature selection from data sets. In this paper, the basic concepts of rough set theory and ant colony optimization are introduced, and the role of the basic constructs of rough set approach in feature selection, namely attribute reduction is studied. Base above research, a rough set and ACO based algorithm for feature selection problems is proposed. Finally, the presented algorithm was tested on UCI data sets and performed effectively.