CONSTRUCTING LEAST SQUARE SUPPORT VECTOR MACHINES ENSEMBLE BASED ON FUZZY INTEGRAL

摘要：

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 dont evaluate the importance degree of the output of individual component SVMs classifier to the final decision.This paper proposes a boosting least square support vectormachine (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.

关键词： LS-SVM SVM ensemble Boosting Fuzzy integral Information fusion

作者: CHUN-MEI LIU LIANG-KUAN ZHU

作者单位: College of Foundation Science, Harbin University of Commerce, Harbin, 150076, China Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2391-2395

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）