A novel SVM ensemble approach using clustering analysis

A novel Support Vector Machine (SVM) ensemble approach using clustering analysis is proposed. Firstly, the positive and negative training examples are clustered through subtractive clustering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last, the outputs of the individual classifiers are fused through majority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches, such as Bagging, Adaboost and k.-fold cross validation, are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during ensemble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.