Title :
Binary classification with noise via fuzzy weighted least squares twin support vector machine
Author :
Juntao Li ; Yimin Cao ; Yadi Wang ; Xiaoxia Mu ; Liuyuan Chen ; Huimin Xiao
Author_Institution :
Coll. of Math. & Inf. Sci., Henan Normal Univ., Xinxiang, China
Abstract :
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.
Keywords :
fuzzy set theory; least squares approximations; pattern classification; support vector machines; UCI data; binary classification; fuzzy weighted least squares twin support vector machine; noise influence reduction; two-moons data; Accuracy; Electronic mail; Ionosphere; Kernel; Liver; Noise; Support vector machines; Weighted support vector machine; binary classification; fuzzy weighted mechanism; least squares twin support vector machine; noise;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162214
