COMBINING MULTIPLE SUPPORT VECTOR MACHINES USING FUZZY INTEGRAL FOR CLASSIFICATION
Recently, in the area of pattern recognition, the concept of combining multiple support vector machines (SVMs) has been proposed as a new direction to improve classification performance. However, current commonly used SVMs aggregation strategies do not evaluate the importance of degree of the output of individual component SVM classifier to the final decision. A method for multiple SVMs combination using fuzzy integral is proposed to resolve this problem. Fuzzy integral combines objective evidence, in the form of a SVM probabilistic output, with subjective evaluation of the importance of that component SVM with respect to the final decision. The experimental results confirm the superiority of the presented method to the traditional majority voting technique.
Support vector machines Multiple support vector machines Bagging Fuzzy integral Majority voting
GEN-TING YAN GUANG-FU MA LIANG-KUAN ZHU ZHONG SHI
Department of control science and engineering, Harbin Institute of Technology, Harbin 150001, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3438-3441
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）