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

Article type: Research Article

Authors: Dong, Guishan^{; *} | Mu, Xuewen

Affiliations: School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi, China

Correspondence: [*] Corresponding author. Guishan Dong, School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi, China. E-mail: [email protected].

Abstract: 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 l2-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β 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.

Keywords: Support vector machine, second-order cone programming, binary data classification

DOI: 10.3233/JIFS-200467

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4505-4513, 2020