Data classification using Support Vector Machine integrated with scatter search method

Support Vector Machine (SVM) is a popular pattern classification method with many diverse applications. The SVM has many parameters, which have significant influences the performance of SVM classifier. In this paper, we employ a meta-heuristic approach (Scatter Search) to find near optimal values of the SVM parameters, and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine, shortly (3SVM). To evaluate the performance of the proposed method, 9 datasets from LibSVM tool webpage [2] were used. Experiments prove that the proposed method is promising and has competitive performance.