A novel second-order cone programming support vector machine model for binary data classification

作     者：Dong, Guishan Mu, Xuewen 

作者机构：Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China 

出 版 物：《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)

年 卷 期：2020年第39卷第3期

页      面：4505-4513页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Support vector machine second-order cone programming binary data classification 

摘      要：The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l(2)-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters alpha and beta are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.