Binary Classification With Noise via Fuzzy Weighted Least Squares Twin Support Vector Machine

被引：0

作者：

Li, Juntao ^{[1
]}

Cao, Yimin ^{[1
]}

Wang, Yadi ^{[1
]}

Mu, Xiaoxia ^{[2
]}

Chen, Liuyuan ^{[3
]}

Xiao, Huimin ^{[4
]}

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

来源：

2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年

关键词：

Weighted support vector machine; least squares twin support vector machine; binary classification; fuzzy weighted mechanism; noise; FEATURE-SELECTION; REGRESSION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：1817 / 1821

页数：5

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