Support vector machine ensemble using rough sets theory

作     者：胡中辉 Cai Yunze He Xing Xu Xiaoming 

作者机构：Department of Automation Shanghai Jiaotong University Shanghai 200030 P.R. China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2006年第12卷第1期

页      面：58-62页

核心收录：

学科分类：07[理学] 08[工学] 070104[理学-应用数学] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the High Technology Research and Development Programme of China (2002AA412010)  and the National Key Basic Research and Development Program of China (2002cb312200) and the National Natural Science Foundation of China (60174038) 

主　　题：support vector machines rough sets ensemble attribute reduction decision fusion 

摘      要：A support vector machine （SVM） ensemble classifier is proposed. Performance of SVM trained in an input space eonsisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decision-making of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.