The Improved Particle Swarm Optimization for Feature Selection of Support Vector Machine

Support vector machine (SVM) is good at classifying high dimensional data. Parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. An improved algorithm based on particle swarm optimization (PSO) for feature selection and parameters optimization of SVM (GPSO-SVM) is proposed to improve the classification accuracy and select the number of features as little as possible. This method introduces crossover and mutation operator from genetic algorithm (GA), which allows the particle to carry out crossover and mutation operations after iteration and update to avoid the problem of falling into local optimum and premature maturation in