Least squares twin support vector machine with asymmetric squared loss  

作　　者：Wu Qing Li Feiyan Zhang Hengchang Fan Jiulun Gao Xiaofeng 

机构地区：[1]School of Automation,Xi'an University of Posts and Telecommunications,Xi'an 710121,China [2]School of Telecommunication and Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2023年第1期1-16,共16页中国邮电高校学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(51875457);Natural Science Foundation of Shaanxi Province of China(2021JQ-701);the Key Research Project of Shaanxi Province(2022GY-050,2022GY-028);Xi’an Science and Technology Plan Project(2020KJRC0109)。

摘　　要：For classification problems,the traditional least squares twin support vector machine(LSTSVM)generates two nonparallel hyperplanes directly by solving two systems of linear equations instead of a pair of quadratic programming problems(QPPs),which makes LSTSVM much faster than the original TSVM.But the standard LSTSVM adopting quadratic loss measured by the minimal distance is sensitive to noise and unstable to re-sampling.To overcome this problem,the expectile distance is taken into consideration to measure the margin between classes and LSTSVM with asymmetric squared loss(aLSTSVM)is proposed.Compared to the original LSTSVM with the quadratic loss,the proposed aLSTSVM not only has comparable computational accuracy,but also performs good properties such as noise insensitivity,scatter minimization and re-sampling stability.Numerical experiments on synthetic datasets,normally distributed clustered(NDC)datasets and University of California,Irvine(UCI)datasets with different noises confirm the great performance and validity of our proposed algorithm.

关 键 词：classification least SQUARES TWIN support VECTOR machine ASYMMETRIC LOSS noise INSENSITIVITY 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]