高技术通讯（英文版）2006，Vol.12Issue(1)：58-62,5.
Support vector machine ensemble using rough sets theory
Support vector machine ensemble using rough sets theory
摘要
Abstract
A support vector machine (SVM) ensemble classifier is proposed. Performance of SVM trained in an input space consisting 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 decisionmaking 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.关键词
support vector machines/rough sets/ensemble/attribute reduction/decision fusionKey words
support vector machines/rough sets/ensemble/attribute reduction/decision fusion分类
信息技术与安全科学引用本文复制引用
..Support vector machine ensemble using rough sets theory[J].高技术通讯（英文版）,2006,12(1):58-62,5.基金项目
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). （2002AA412010）
