代价敏感分类器的比较研究(英文)

A Comparative Study of Cost-Sensitive Classifiers

简要地回顾了代价敏感学习的理论和现有的代价敏感学习算法.将代价敏感学习算法分为两类,分别是直接代价敏感学习和代价敏感元学习,其中代价敏感元学习可以将代价不敏感的分类器转换为代价敏感的分类器.提出了一种简单、通用、有效的元学习算法,称为经验阈值调整算法(简称ETA).评估了各种代价敏感元学习算法和ETA的性能.ETA几乎总是得到最低的误分类代价,而且它对误分类代价率最不敏感.还得到了一些关于元学习的其它有用结论.文章是"Thresholding for Making Classifiers Cost-sensitive"的改进和扩展版本,原文章由Victor S.Sheng和Charles X.Ling完成,发表于AAAI2006国际会议.

The authors briefly review the theory of cost-sensitive learning, and the existing cost-sensitive learning algorithms. The authors categorize cost-sensitive learning algorithms into direct cost-sensitive learning and cost-sensitive meta-learning, which converts cost-insensitive classifiers into cost-sensitive ones. The authors also propose a simple yet general and effective meta-learning method called Empirical Threshold Adjusting （ETA for short）. The authors evaluate the performance of various cost-sensitive meta-learning algorithms including ETA. ETA almost always produces the lowest misclassification cost, and is least sensitive to the misclassification cost ratio. Other useful conclusions on cost-sensitive meta-learning methods are drawn.