An improvement on twin parametric-margin support vector machine

Sahleh, Ali; Salahi, Maziar; Eskandari, Sadegh

doi:10.22105/dmor.2021.281369.1372

Document Type : Original Article

Authors

¹ Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran.

² Department of Computer Science, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran.

https://doi.org/10.22105/dmor.2021.281369.1372

Abstract

Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.
Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane of the other class.
Findings: The enhanced model is convex for both linear and nonlinear cases. Also, numerical experiments on UCI datasets show that the enhanced model performs better compared to two similar models for both linear and nonlinear cases.
Originality/Value: The previous studies of TPMSVM that increased the accuracy through approaches such as assigning weights to data sample, converting it into an unconstrained model and adding a new term in the objective function, did not guarantee that all samples will be far and on the negative side of the margin hyperplane. However, this study provides an approach to overcome this disadvantage of TPMSVM.

Keywords

20.1001.1.25385097.1401.7.4.1.2

Main Subjects

stochastic/Probabilistic/fuzzy/dynamic modeling

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Journal of Decisions and Operations Research

An improvement on twin parametric-margin support vector machine

References

References

Volume 7, Issue 4
November 2022
Pages 503-514

An improvement on twin parametric-margin support vector machine

References

References

Volume 7, Issue 4November 2022Pages 503-514

Volume 7, Issue 4
November 2022
Pages 503-514