caa a22 4500 605469687 CHVBK 20210128100323.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00500-014-1448-9 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1448-9 A bilateral-truncated-loss based robust support vector machine for classification problems [Elektronische Daten] [Xiaowei Yang, Le Han, Yan Li, Lifang He] Support vector machine (SVM) is sensitive to outliers or noise in the training dataset. Fuzzy SVM (FSVM) and the bilateral-weighted FSVM (BW-FSVM) can partly overcome this shortcoming by assigning different fuzzy membership degrees to different training samples. However, it is a difficult task to set the fuzzy membership degrees of the training samples. To avoid setting fuzzy membership degrees, from the beginning of the BW-FSVM model, this paper outlines the construction of a bilateral-truncated-loss based robust SVM (BTL-RSVM) model for classification problems with noise. Based on its equivalent model, we theoretically analyze the reason why the robustness of BTL-RSVM is higher than that of SVM and BW-FSVM. To solve the proposed BTL-RSVM model, we propose an iterative algorithm based on the concave-convex procedure and the Newton-Armijo algorithm. A set of experiments is conducted on ten real world benchmark datasets to test the robustness of BTL-RSVM. The statistical tests of the experimental results indicate that compared with SVM, FSVM and BW-FSVM, the proposed BTL-RSVM can significantly reduce the effects of noise and provide superior robustness. Springer-Verlag Berlin Heidelberg, 2014 Bilateral-weighted fuzzy support vector machine nationallicence Support vector machine nationallicence Fuzzy support vector machine nationallicence Concave-convex procedure nationallicence Class noise nationallicence Yang Xiaowei Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Han Le Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Li Yan Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut He Lifang School of Computer Science and Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2871-2882 1432-7643 19:10<2871 2015 19 500 https://doi.org/10.1007/s00500-014-1448-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1448-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yang Xiaowei Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Han Le Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Li Yan Department of Mathematics, School of Sciences, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 700 1- He Lifang School of Computer Science and Engineering, South China University of Technology, 510641, Guangzhou, People's Republic of China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2871-2882 1432-7643 19:10<2871 2015 19 500