On the foundations of noise-free selective classification

被引：0

作者：

El-Yaniv, Ran ^{[1
]}

Wiener, Yair ^{[1
]}

机构：

[1] Computer Science Department Technion, Israel Institute of Technology, Haifa 32000, Israel

来源：

Journal of Machine Learning Research | 2010年 / 11卷

关键词：

D O I：

暂无

中图分类号：

C [社会科学总论];

学科分类号：

03 ; 0303 ;

摘要：

We consider selective classification, a term we adopt here to refer to 'classification with a reject option.' The essence in selective classification is to trade-off classifier coverage for higher accuracy. We term this trade-off the risk-coverage (RC) trade-off. Our main objective is to characterize this trade-off and to construct algorithms that can optimally or near optimally achieve the best possible trade-offs in a controlled manner. For noise-free models we present in this paper a thorough analysis of selective classification including characterizations of RC trade-offs in various interesting settings. © 2010 Ran El-Yaniv and Yair Wiener.

引用

页码：1605 / 1641