CAJ | 학술논문

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.
간요지회고료대개민감학습적이론화현유적대개민감학습산법.장대개민감학습산법분위량류,분별시직접대개민감학습화대개민감원학습,기중대개민감원학습가이장대개불민감적분류기전환위대개민감적분류기.제출료일충간단、통용、유효적원학습산법,칭위경험역치조정산법(간칭ETA).평고료각충대개민감원학습산법화ETA적성능.ETA궤호총시득도최저적오분류대개,이차타대오분류대개솔최불민감.환득도료일사관우원학습적기타유용결론.
The authors briefly review the theory of cost-sensitive learning, and the existing costsensitive 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.This is an improved and expanded version of the paper "Thresholding for Making Classifiers Cost-sensitive" by Victor S. Sheng and Charles X. Ling, published in AAAI 2006.