BriefGPT.xyz
Aug, 2012
AUC 两两优化的一致性
On the consistency of optimizing AUC
HTML
PDF
Wei Gao, Zhi-Hua Zhou
TL;DR
本文提供了用于鉴定基于替代损失函数的学习方法渐近一致性的充分条件,并证明了指数损失和逻辑损失与AUC一致,但铰链损失是不一致的。基于这个结果,本文还推导了一些与AUC一致的损失函数,进一步揭示了指数损失和逻辑损失的相容界限以及在非噪声设置下许多替代损失函数的相容界限,并发现AdaBoost和RankBoost具有相同的指数代理损失。
Abstract
auc
(area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many
learning appro
→