Opposition-based binary competitive optimization algorithm using time-varying V-shape transfer function for feature selection

The feature selection problem is known as one of the most critical issues with several applications in classification. Certain features of a dataset do not usually contain useful information or play important roles in the data classification. It is possible to reduce the computational burden through the elimination of unnecessary features. This paper was conducted to propose an opposition-based binary competitive optimization algorithm, namely OBCOOA, to solve the wrapper-based feature selection problems. The basic idea of the competitive optimization algorithm (COOA) lies on the natural competition between different species to survive, which is a continuous algorithm inherently. The present study entailed the main contributions as follows: primarily, a time-varying V-shape transfer function was used in the optimization process to present the binary version of the COOA algorithm, which established the right balance between the exploration and exploitation phases; secondly, in the proposed algorithm, the opposition-based learning mechanism was utilized to improve the diversity quality within the population members and to incorporate a suitable initial population. In the feature selection problem, the classification error rate and the number of selected features objective functions are usually adopted, which causes this problem to convert into a multi-objective optimization one. This research presented a single- and multi-objective approach from the proposed algorithm in order to solve the feature selection problem. The proposed algorithm was applied to 27 benchmark datasets, and the evaluation results were compared to the other well-known binary optimization algorithms. The experimental results indicated that the proposed algorithm has a better ability to find an optimal subset of features with the least classification error rate and the number of the selected features.