Constructing Least Square Support Vector Machines Ensemble Based on Fuzzy Integral
Chunmei Liu, Liangkuan Zhu · 2006
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