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Adaptively weighted large-margin angle-based classifiers

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  • Fu, Sheng
  • Zhang, Sanguo
  • Liu, Yufeng

Abstract

Large-margin classifiers are powerful techniques for classification problems. Although binary large-margin classifiers are heavily studied, multicategory problems are more complicated and challenging. A common approach is to construct k different decision functions for a k-class problem with a sum-to-zero constraint. However, such a constraint can be inefficient. Moreover, many large-margin classifiers can be sensitive to outliers in the training sample. In this article, we use the angle-based classification framework to avoid the explicit sum-to-zero constraint, and we propose two adaptively weighted large-margin classification techniques. Our new methods are Fisher consistent and more robust against outliers under suitable conditions. Numerical experiments further indicate that our methods give competitive and stable performance when compared with existing approaches.

Suggested Citation

  • Fu, Sheng & Zhang, Sanguo & Liu, Yufeng, 2018. "Adaptively weighted large-margin angle-based classifiers," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 282-299.
    • Handle: RePEc:eee:jmvana:v:166:y:2018:i:c:p:282-299
    DOI: 10.1016/j.jmva.2018.03.004
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    References listed on IDEAS

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    1. Chong Zhang & Yufeng Liu, 2014. "Multicategory angle-based large-margin classification," Biometrika, Biometrika Trust, vol. 101(3), pages 625-640.
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    4. Liu, Yufeng & Zhang, Hao Helen & Wu, Yichao, 2011. "Hard or Soft Classification? Large-Margin Unified Machines," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 166-177.
    5. YichaoWu, & Liu, Yufeng, 2007. "Robust Truncated Hinge Loss Support Vector Machines," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 974-983, September.
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    Cited by:

    1. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).

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