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Multi-class nonparallel support vector machine

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Abstract

In this paper, we propose an extended version of Nonparallel Support Vector Machine (NPSVM) for multi-classification using one-versus-one-versus-rest approach called MCNPSVM. The MCNPSVM is converted into a series of binary classification problems, in each of which two nonparallel hyperplanes are found by solving two quadratic programming problems. This is done in such a way that each hyperplane is aligned with the data points of the class that it represents by constructing an \(\epsilon \)-insensitive tube and is as far as possible from the other class, while the rest of the data are in the margin of these two hyperplanes. Further, in order to accelerate learning time of MCNPSVM, the mean of data matrix corresponding to the rest classes is used. Experiments on benchmark datasets are executed to study the performance of proposed models compared to various multi-class SVM extensions.

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Authors and Affiliations

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

    Ali Sahleh & Maziar Salahi

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

    Sadegh Eskandari

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  1. Ali Sahleh
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  2. Maziar Salahi
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Correspondence to Maziar Salahi.

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Sahleh, A., Salahi, M. & Eskandari, S. Multi-class nonparallel support vector machine. Prog Artif Intell 12, 349–361 (2023). https://doi.org/10.1007/s13748-023-00308-7

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