Constructing Least Square Support Vector Machines Ensemble Based on Fuzzy Integral

Even the support vector machine (SVM) has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level because they don´t evaluate the importance degree of the output of individual component SVMs classifier to the final decision. This paper proposes a boosting least square support vector machine (LS-SVM) ensemble method based on fuzzy integral to improve the limited classification performance. In general, the proposed method is built in 3 steps: construct the component LS-SVM; obtain the probabilistic outputs model of each component LS-SVM; combine the component predictions based on fuzzy integral. The trained individual LS-SVMs are aggregated to make a final decision. The simulating results demonstrate that the proposed LS-SVM ensemble with boosting outperforms a single SVM and traditional SVM (or LS-SVM) ensemble technique via majority voting in terms of classification accuracy