Constructing Multiple Support Vector Machines Ensemble Based onFuzzy Integral and Rough Reducts

摘要：

Even the multiple support vector machine (SVM) ensemble 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. As compared to traditional Bagging and Boosting methods, this paper proposes a novel SVM ensemble method based on fuzzy integral and rough reducts. In general, the proposed method is built in 3 steps: construct the individual SVM of ensemble by rough reduction technique; obtain the probabilistic outputs model of each component SVM; combine the component predictions based on fuzzy integral. The trained individual SVMs are aggregated to make a final decision. The simulating results demonstrate that the proposed multiple SVM ensemble method outperforms a single SVM and traditional SVM ensemble technique via Bagging and Boosting in terms of classification accuracy.

作者: Yi-zhuo ZHANG Chun-mei LIU Liang-kuan ZHU Qing-lei HU

作者单位: Northeast Forestry University, China Harbin University of Commerce, China Harbin Institute of Technology, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）