Chaotic African Vultures Optimization Algorithm for Feature Selection

Abstract

Feature selection is a widely used technique to remove the undesirable, noisy and inaccurate information from raw input dataset while maintaining the accuracy and efficiency of classifier. Tremendous researches have explored the feasibility of metaheuristic search algorithms (MSAs) such as African Vultures Optimization Algorithm (AVOA) to solve feature selection problem. Similar with many original MSAs, the conventional initialization scheme of AVOA has undesirable drawbacks that can lead to entrapment of local optima, especially when dealing with complex dataset. In this paper, a new variant known as Chaotic African Vultures Optimization Algorithm (CAVOA) is proposed to solve feature selection problem with enhanced classification accuracy by incorporating the chaotic map concept into the initialization scheme. Twelve datasets obtained from UCI Machine Learning Repository are used to investigate the capability of CAVOA in feature selection and compared with four other peer algorithms. Simulation results show that CAVOA can produce the best classification accuracies and lowest feature numbers in most datasets.