文献类型：会议论文

英文题名：Binary Classification With Noise via Fuzzy Weighted Least Squares Twin Support Vector Machine

作者：Li, Juntao[1];Cao, Yimin[1];Wang, Yadi[1];Mu, Xiaoxia[2];Chen, Liuyuan[3];Xiao, Huimin[4]

第一作者：Li, Juntao

通讯作者：Li, JT[1]

机构：[1]Henan Normal Univ, Coll Math & Informat Sci, Xinxiang 453007, Peoples R China;[2]Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Peoples R China;[3]Henan Normal Univ, Journal Editorial Dept, Xinxiang 453007, Peoples R China;[4]Henan Univ Econ & Law, Dept Math & Informat Sci, Zhengzhou 450002, Peoples R China

第一机构：Henan Normal Univ, Coll Math & Informat Sci, Xinxiang 453007, Peoples R China

通讯机构：[1]corresponding author), Henan Normal Univ, Coll Math & Informat Sci, Xinxiang 453007, Peoples R China.

会议论文集：27th Chinese Control and Decision Conference (CCDC)

会议日期：MAY 23-25, 2015

会议地点：Qingdao, PEOPLES R CHINA

语种：英文

外文关键词：Weighted support vector machine; least squares twin support vector machine; binary classification; fuzzy weighted mechanism; noise

摘要：A new weighted least squares twin support vector machine for binary classification with noise is proposed in this paper. By using the distances from the sample points to their class center, fuzzy weights are constructed. The fuzzy weighted least squares twin support vector machine is presented by following the fuzzy weighted mechanism, thus reducing the influence of the noise. The simulation results on three UCI data and two-moons data demonstrate the effectiveness of the proposed method.