Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 3; pp. 2634 - 2654
• Main Authors: Moosaei, Hossein, Bazikar, Fatemeh, Ketabchi, Saeed, Hladík, Milan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Computer Science; Convex analysis; Datasets; Handwriting recognition; Machines; Manufacturing; Mechanical Engineering; Mine detection; Newton methods; Optimization; Processes; Support vector machines; Par; Universum; support vector machine; Nonconvex optimization; DCA; Modified Newton method; DC programming; BDCA
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-021-02402-6

Universum data that do not belong to any class of a classification problem can be exploited to utilize prior knowledge to improve generalization performance. In this paper, we design a novel parametric ν -support vector machine with universum data ( U Par- ν -SVM). Unlabeled samples can be integrated into supervised learning by means of U Par- ν -SVM. We propose a fast method to solve the suggested problem of U Par- ν -SVM. The primal problem of U Par- ν -SVM, which is a nonconvex optimization problem, is transformed into an unconstrained optimization problem so that the objective function can be treated as a difference of two convex functions (DC). To solve this unconstrained problem, a boosted difference of convex functions algorithm (BDCA) based on a generalized Newton method is suggested (named DC- U Par- ν -SVM). We examined our approach on UCI benchmark data sets, NDC data sets, a handwritten digit recognition data set, and a landmine detection data set. The experimental results confirmed the effectiveness and superiority of the proposed method for solving classification problems in comparison with other methods.