A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). Nevertheless, BPSO has limitations of premature convergence and the setting of inertia weight. Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive performance in feature selection, which is appropriate for application in engineering, rehabilitation and clinical areas.