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SOCP approach to robust twin parametric margin support vector machine

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Abstract

Twin parametric-margin support vector machine (TPMSVM) is one of the classification tools based on parametric margin ν-SVM (par-ν-SVM) and twin SVM (TWSVM) which is useful for the data having heteroscedastic error structure. However, TPMSVM does not take into account the data uncertainty that might affect model accuracy. In this paper, we study TPMSVM under ellipsoidal uncertainty and its robust counterpart is presented as a second-order cone program (SOCP). Moreover, the robust formulation is extended to the nonlinear case using Gaussian kernels. To evaluate the efficiency of the proposed robust models, experiments have been conducted on nineteen University of California Irvine (UCI) datasets and the results have been compared with TWSVM, twin minimax probability machine classification (TMPMC), TPMSVM, Robust parametric TWSVM (RPTWSVM), and Twin SOCP−SVM (TSOCP−SVM) in the literature. The results show that the new model yieldsbetter classification accuracy.

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Acknowledgements

The authors would like to thank the editor and reviewers for their useful comments and suggestions.

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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. Center of Excellence for Mathematical Modeling, Optimization and Combinatorial Computing (MMOCC) University of Guilan, Rasht, Iran

    Maziar Salahi

  3. 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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  3. Sadegh Eskandari
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Correspondence to Maziar Salahi.

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Sahleh, A., Salahi, M. & Eskandari, S. SOCP approach to robust twin parametric margin support vector machine. Appl Intell 52, 9174–9192 (2022). https://doi.org/10.1007/s10489-021-02859-5

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