文献类型：期刊文献

英文题名：Least squares twin bounded support vector machines based on L1-norm distance metric for classification

作者：Yan, He[1] Ye, Qiaolin[1,4,5] Zhang, Tian'an[1,2] Yu, Dong-Jun[3] Yuan, Xia[3] Xu, Yiqing[1] Fu, Liyong[6]

第一作者：Yan, He

通信作者：Ye, QL[1];Ye, QL[2]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, 159 Longpan Rd, Nanjing 210037, Jiangsu, Peoples R China;[2]Nanjing Forestry Univ, Collaborat Innovat Ctr Sustainable Forestry South, 159 Longpan Rd, Nanjing 210037, Jiangsu, Peoples R China;[3]Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Xiaolingwei 200, Nanjing 210094, Jiangsu, Peoples R China;[4]Nanjing Univ Sci & Technol, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Jiangsu, Peoples R China;[5]Huaiyin Inst Technol, Lab Internet Things & Mobile Internet Technol Jia, Huaian 223003, Peoples R China;[6]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2018

卷号：74

起止页码：434-447

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20174404321833);Scopus(收录号：2-s2.0-85032265090);WOS:【SCI-EXPANDED(收录号:WOS:000417547800032)】；

基金：The work is supported in part by the National Science Foundation of China under Grants 61401214, 61773210, 61603184, 61603190 and 61772273, the Natural Science Foundation of Jiangsu Province under Grants BK20171453, BK20140794, the Jiangsu Key Laboratory for Internet of Things and Mobile Internet Technology, and the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety.

语种：英文

外文关键词：L1-LSTBSVM; TBSVM; L1-norm distance; Outliers

摘要：In this paper, we construct a least squares version of the recently proposed twin bounded support vector machine (TBSVM) for binary classification. As a valid classification tool, TBSVM attempts to seek two non-parallel planes that can be produced by solving a pair of quadratic programming problems (QPPs), but this is time-consuming. Here, we solve two systems of linear equations rather than two QPPs to avoid this deficiency. Furthermore, the distance in least squares TBSVM (LSTBSVM) is measured by L2-norm, but L1-norm distance is usually regarded as an alternative to L2-norm to improve model robustness in the presence of outliers. Inspired by the advantages of least squares twin support vector machine (LST-WSVM), TBSVM and L1-norm distance, we propose a LSTBSVM based on L1-norm distance metric for binary classification, termed as L1-LSTBSVM, which is specially designed for suppressing the negative effect of outliers and improving computational efficiency in large datasets. Then, we design a powerful iterative algorithm to solve the L1-norm optimal problems, and it is easy to implement and its convergence to an optimum solution is theoretically ensured. Finally, the feasibility and effectiveness of L1-LSTBSVM are validated by extensive experimental results on both UCI datasets and artificial datasets. (C) 2017 Elsevier Ltd. All rights reserved.