论文标题
基于角度的成本敏感性分类
Angle-Based Cost-Sensitive Multicategory Classification
论文作者
论文摘要
许多现实世界的分类问题都带有成本，这些成本可能会因不同类型的错误分类而有所不同。因此，重要的是要开发对成本敏感的分类器，从而最大程度地减少总分类成本。尽管二进制成本敏感的分类器经过了很好的研究，但是解决多材分类问题仍然具有挑战性。解决此问题的一种流行方法是为K级问题构建K分类功能，并通过施加总和到零约束来删除冗余。但是，这种方法通常会导致更高的计算复杂性和效率低下的算法。在本文中，我们提出了一个新型的基于角度的成本敏感分类框架，用于多酸性分类，而无需总和零约束。基于角度的成本敏感分类框架中包含的损失功能进一步证明是Fisher一致的。为了显示框架的有用性，将两个具有成本敏感的多材促进算法得出作为具体实例。数值实验表明，提出的增强算法对其他现有增强方法产生了竞争性分类性能。
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 paper, 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 proposed boosting algorithms yield competitive classification performances against other existing boosting approaches.
