![]() The results were analyzed based on classifier performance and the selected number of features. Five benchmark datasets were used to implement the method. This paper attempts to improve the velocity-based initialization (VBR) method on the feature selection problem using support vector machine classifier following the wrapper method strategy. However, the standard PSO algorithm suffers from premature convergence, a condition whereby PSO tends to get trapped in a local optimum that prevents it from being converged to a better position. The use of particle swarm optimization (PSO) as the feature selection method was found to be competitive than its optimization counterpart. ![]() ![]() ![]() The performance of feature selection method is typically measured based on the accuracy and the number of selected features. ![]()
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