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Value-at-risk support vector machine: stability to outliers

Author

Listed:
  • Peter Tsyurmasto

    (University of Florida)

  • Michael Zabarankin

    (Stevens Institute of Technology)

  • Stan Uryasev

    (University of Florida)

Abstract

A support vector machine (SVM) stable to data outliers is proposed in three closely related formulations, and relationships between those formulations are established. The SVM is based on the value-at-risk (VaR) measure, which discards a specified percentage of data viewed as outliers (extreme samples), and is referred to as $$\mathrm{VaR}$$ VaR -SVM. Computational experiments show that compared to the $$\nu $$ ν -SVM, the VaR-SVM has a superior out-of-sample performance on datasets with outliers.

Suggested Citation

  • Peter Tsyurmasto & Michael Zabarankin & Stan Uryasev, 2014. "Value-at-risk support vector machine: stability to outliers," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 218-232, July.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:1:d:10.1007_s10878-013-9678-9
    DOI: 10.1007/s10878-013-9678-9
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    References listed on IDEAS

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    1. Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
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    Cited by:

    1. Xiaoqian Zu & Yongxiang Wu & Zhenduo Zhang & Lu Yu, 2019. "Prediction of Consumption Choices of Low-Income Groups in a Mixed-Income Community Using a Support Vector Machine Method," Sustainability, MDPI, vol. 11(14), pages 1-12, July.
    2. Roman V. Ivanov, 2018. "A Credit-Risk Valuation under the Variance-Gamma Asset Return," Risks, MDPI, vol. 6(2), pages 1-25, May.
    3. Ivanov Roman V., 2018. "On risk measuring in the variance-gamma model," Statistics & Risk Modeling, De Gruyter, vol. 35(1-2), pages 23-33, January.
    4. He Huang & Wei Gao & Chunming Ye, 0. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-12.
    5. He Huang & Wei Gao & Chunming Ye, 2021. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 884-895, November.

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