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A New Support Vector Machine Plus with Pinball Loss

Author

Listed:
  • Wenxin Zhu

    (TianJin Agricultural University)

  • Yunyan Song

    (Tianjin University of Technology)

  • Yingyuan Xiao

    (Tianjin University of Technology
    Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology)

Abstract

The hinge loss support vector machine (SVM) is sensitive to outliers. This paper proposes a new support vector machine with a pinball loss function (PSVM+). The new model is less sensitive to noise, especially the feature noise around the decision boundary. Furthermore, the PSVM+ is more stable than the hinge loss support vector machine plus (SVM+) for re-sampling. It also embeds the additional information into the corresponding optimization problem, which is helpful to further improve the learning performance. Meanwhile, the computational complexity of the PSVM+ is similar to that of the SVM+.

Suggested Citation

  • Wenxin Zhu & Yunyan Song & Yingyuan Xiao, 2018. "A New Support Vector Machine Plus with Pinball Loss," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 52-70, April.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:1:d:10.1007_s00357-018-9249-y
    DOI: 10.1007/s00357-018-9249-y
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    References listed on IDEAS

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    1. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
    2. 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. Douglas L. Steinley, 2019. "Editorial: Journal of Classification Vol. 36-3," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 393-396, October.

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