Optimizing LSSVM Parameters Using PSO Algorithm

This article presents a methodology for optimizing LSSVM parameters using Particle Swarm Optimization (PSO) to improve classification accuracy. The implementation involves initializing particles representing parameter combinations (typically regularization parameter gamma and RBF kernel parameter sigma), where each particle's position corresponds to specific parameter values. The PSO algorithm iteratively updates particle velocities and positions based on personal best and global best solutions, converging toward optimal parameters that minimize classification error. Through this optimization approach, we can systematically enhance model performance by finding parameter combinations that maximize separation between classes while minimizing overfitting. The optimized LSSVM model demonstrates improved generalization capability on complex datasets, providing more reliable foundations for decision-making and predictive analytics. Key implementation steps include defining fitness functions using cross-validation accuracy, setting PSO parameters like inertia weight and acceleration constants, and validating results with confusion matrices and ROC curves. This parameter optimization strategy enables robust handling of real-world classification challenges, particularly when dealing with high-dimensional or imbalanced datasets. By integrating PSO's global search capabilities with LSSVM's structural risk minimization principle, we achieve a balanced trade-off between model complexity and predictive performance, ultimately yielding more accurate and interpretable classification outcomes for practical applications.