Angle-based cost-sensitive multicategory classification

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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The article introduces a novel angle-based cost-sensitive classification framework for multicategory classification without the sum-to-zero constraint, with loss functions proven to be Fisher consistent. Two cost-sensitive multicategory boosting algorithms derived from the framework show competitive classification performances in numerical experiments against other existing boosting approaches.