Novel Algorithm for Constructing Support Vector Machines Classification Ensemble

Ensemble classification has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. And Support vector machines (SVMs) ensemble has been proposed to improve classification accuracy recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. In this research, a novel SVMs ensemble algorithm based on fuzzy choquet integral is proposed in this paper to deal with this problem. This method aggregates the outputs of separate component SVMs with importance of each component SVM, which is subjectively assigned as the nature of fuzzy logic. The simulating results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy.