Leveraging Opposition-Based Learning in Particle Swarm Optimization for Effective Feature Selection

Feature selection serves as a critical preprocessing step in machine learning, focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms. Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges. However, there are inherent limitations in Particle Swarm Optimization, such as the delicate balance between exploration and exploitation, susceptibility to local optima, and suboptimal convergence rates, hinder its performance. To tackle these issues, this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization, tailored for wrapper-based feature selection. The proposed approach integrates: (1) a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation, (2) the lever principle within Opposition-Based Learning to improve search efficiency, and (3) a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset. The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advanced metaheuristic algorithms. Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets, whilst also significantly reducing the number of selected features. These findings demonstrate its effectiveness and robustness in feature selection tasks.