Multi-class support vector machine based on minimization of reciprocal-geometric-margin norms

Yoshifumi Kusunoki and Keiji Tatsumi

European Journal of Operational Research, 2025, vol. 324, issue 2, 580-589

Abstract: In this paper, we propose a Support Vector Machine (SVM) method for multi-class classification. It follows multi-objective multi-class SVM (MMSVM), which maximizes class-pair margins on a multi-class linear classifier. The proposed method, called reciprocal-geometric-margin-norm SVM (RGMNSVM) is derived by applying the ℓp-norm scalarization and convex approximation to MMSVM. Additionally, we develop the margin theory for multi-class linear classification, in order to justify minimization of reciprocal class-pair geometric margins. Experimental results on synthetic datasets explain situations where the proposed RGMNSVM successfully works, while conventional multi-class SVMs fail to fit underlying distributions. Results of classification performance evaluation using benchmark data sets show that RGMNSVM is generally comparable with conventional multi-class SVMs. However, we observe that the proposed approach to geometric margin maximization actually performs better classification accuracy for certain real-world data sets.

Keywords: Machine learning; Support vector machine; Multi-class classification; Geometric margin maximization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:324:y:2025:i:2:p:580-589

DOI: 10.1016/j.ejor.2025.03.028

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