Feature Selection using PSO-SVM
نویسندگان
چکیده
method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and support vector machines (SVMs) with the one-versus-rest method serve as a fitness function of PSO for the classification problem. The proposed method is applied to five classification problems from the literature. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to the other feature selection methods.
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