MATLAB Function for Feature Selection Using Particle Swarm Optimization (PSO)

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Resource Overview

This MATLAB function implements Particle Swarm Optimization (PSO) for feature selection, offering customizable optimization direction, population size, iteration count, and other parameters with detailed algorithm implementation insights.

Detailed Documentation

This MATLAB function implements Particle Swarm Optimization (PSO) for feature selection tasks. Users can customize optimization parameters including objective function direction (minimization/maximization), population size, iteration limits, and other algorithmic settings. The implementation features position-based binary encoding for feature subset representation, velocity updates with inertia weight adjustment, and fitness evaluation using wrapper-based classification performance metrics. The flexible architecture supports various objective functions like classification accuracy or mutual information, making it adaptable to diverse applications such as biomedical data analysis or image processing. Key functions include pso_initialization() for swarm generation, evaluate_fitness() for subset scoring, and update_velocities() with constriction factor handling to prevent premature convergence.

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