计算机工程与应用Issue(22)：150-153,4.DOI:10.3778/j.issn.1002-8331.1311-0260
双隶属度模糊粗糙支持向量机
Fuzzy rough support vector machine with dual membership
摘要
Abstract
It is difficult for support vector machine to deal with uncertain information because SVM is not only sensitive to noises and outliers but also the inconsistence between conditional features and decision labels is not taken into account. In order to overcome the problem, two types of membership are introduced into standard support vector machine, one type of membership is computed by the distance between the training samples and their center as fuzzy membership, the other type of membership is computed by the distance between the training samples and the nearest training sample with different class label as rough membership. At last several comparative experiments are made to show the performance and the validity of the proposed approach.关键词
支持向量机/不确定问题/模糊理论/粗糙集Key words
support vector machine/uncertain problem/fuzzy theory/rough set分类
信息技术与安全科学引用本文复制引用
韩虎,党建武..双隶属度模糊粗糙支持向量机[J].计算机工程与应用,2015,(22):150-153,4.基金项目
甘肃省自然基金（No.1308RJZA224）；兰州交通大学青年基金资助项目（No.2011024）。 （）
