Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

In this paper, we investigate feature subset selection problem by a new self-adaptive firefly algorithm (FA), which is denoted as DbFAFS. In classical FA, it uses constant control parameters to solve different problems, which results in the premature of FA and the fireflies to be trapped in local regions without potential ability to explore new search space. To conquer the drawbacks of FA, we introduce two novel parameter selection strategies involving the dynamical regulation of the light absorption coefficient and the randomization control parameter. Additionally, as an important issue of feature subset selection problem, the objective function has a great effect on the selection of features. In this paper, we propose a criterion based on mutual information, and the criterion can not only measure the correlation between two features selected by a firefly but also determine the emendation of features among the achieved feature subset. The proposed approach is compared with differential evolution, genetic algorithm, and two versions of particle swarm optimization algorithm on several benchmark datasets. The results demonstrate that the proposed DbFAFS is efficient and competitive in both classification accuracy and computational performance.