On the Foundations of Noise-Free Selective Classification
Abstract
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.
Cite
Text
El-Yaniv and Wiener. "On the Foundations of Noise-Free Selective Classification." Journal of Machine Learning Research, 2010.Markdown
[El-Yaniv and Wiener. "On the Foundations of Noise-Free Selective Classification." Journal of Machine Learning Research, 2010.](https://mlanthology.org/jmlr/2010/elyaniv2010jmlr-foundations/)BibTeX
@article{elyaniv2010jmlr-foundations,
title = {{On the Foundations of Noise-Free Selective Classification}},
author = {El-Yaniv, Ran and Wiener, Yair},
journal = {Journal of Machine Learning Research},
year = {2010},
pages = {1605-1641},
volume = {11},
url = {https://mlanthology.org/jmlr/2010/elyaniv2010jmlr-foundations/}
}