A Comparative Study on Binary Artificial Bee Colony Optimization Methods for Feature Selection

Abstract

Feature selection is a major pre-processing technique which aims to pick out distinctive features from whole dataset. In this way it is intended to reduce computational cost of the classification process. Artificial Bee Colony (ABC) algorithm is an evolutionary based swarm intelligence optimization method. In this study, some of the variants of binary ABC algorithms are implemented to the feature selection problem using 10 UCI datasets. The results show that ABC algorithm is useful for this area.