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Angle-based cost-sensitive multicategory classification

Author

Listed:
  • Yang, Yi
  • Guo, Yuxuan
  • Chang, Xiangyu

Abstract

Many real-world classification problems come with costs which can vary for different types of misclassification. It is thus important to develop cost-sensitive classifiers which minimize the total misclassification cost. Although binary cost-sensitive classifiers have been well-studied, solving multicategory classification problems is still challenging. A popular approach to address this issue is to construct K classification functions for a K-class problem and remove the redundancy by imposing a sum-to-zero constraint. However, such method usually results in higher computational complexity and inefficient algorithms. In this article, we propose a novel angle-based cost-sensitive classification framework for multicategory classification without the sum-to-zero constraint. Loss functions that included in the angle-based cost-sensitive classification framework are further justified to be Fisher consistent. To show the usefulness of the framework, two cost-sensitive multicategory boosting algorithms are derived as concrete instances. Numerical experiments demonstrate that the proposed boosting algorithms yield competitive classification performances against other existing boosting approaches.

Suggested Citation

  • Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  • Handle: RePEc:eee:csdana:v:156:y:2021:i:c:s0167947320301985
    DOI: 10.1016/j.csda.2020.107107
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    References listed on IDEAS

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