On the Foundations of Noise-free Selective Classification

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.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Ran El-Yaniv, Yair Wiener
• Keywords: JMLR 2010, Selective Classification, Trade-off, Various Interesting Settings