Clonal Selection Algorithm for Feature Selection and Parameters Optimization of Support Vector Machines

This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of support vector machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to improve classification accuracy, the clonal selection algorithm and genetic algorithm are used to reach the optimization performances with several real-world datasets. The experiments show the effectiveness of the methods. And those results are compared each other. The experiments denote that the proposed clonal selection algorithm is shown to be an evolutionary strategy capable of improving the classification accuracy and has fewer features for support vector machines.