文献类型：期刊文献

英文题名：Robust capped L1-norm twin support vector machine

作者：Wang, Chunyan[1,2] Ye, Qiaolin[1] Luo, Peng[2] Ye, Ning[1] Fu, Liyong[2]

第一作者：Wang, Chunyan

通信作者：Fu, LY[1]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2019

卷号：114

起止页码：47-59

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20191106636770);Scopus(收录号：2-s2.0-85062853283);WOS:【SCI-EXPANDED(收录号:WOS:000466610500005)】；

基金：This work was supported in part by the Thirteenth Five-year Plan Pioneering project of High Technology Plan of the National Department of Technology, China under grant 2017YFC0503906, the National Science Foundation of China under Grant 61871444, the Natural Science Foundation of Jiangsu Province, China under Grant BK20171453, and the Qinglan and Six Talent Peaks Project of Jiangsu Province, China.

语种：英文

外文关键词：Machine learning; TWSVM; Capped L1-norm; Robustness

摘要：Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers. In this paper, we propose a new robust capped L1-norm twin support vector machine (CTWSVM), which sustains the advantages of TWSVM and promotes the robustness in solving a binary classification problem with outliers. The solution of the proposed method can be achieved by optimizing a pair of capped L1-norm related problems using a newly-designed effective iterative algorithm. Also, we present some theoretical analysis on existence of local optimum and convergence of the algorithm. Extensive experiments on an artificial dataset and several UCI datasets demonstrate the robustness and feasibility of our proposed CTWSVM. (c) 2019 Elsevier Ltd. All rights reserved.