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Classification with confidence

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  • Jing Lei

Abstract

A framework for classification is developed with a notion of confidence. In this framework, a classifier consists of two tolerance regions in the predictor space, with a specified coverage level for each class. The classifier also produces an ambiguous region where the classification needs further investigation. Theoretical analysis reveals interesting structures of the confidence-ambiguity trade-off, and the optimal solution is characterized by extending the Neyman–Pearson lemma. We provide general estimating procedures, along with rates of convergence, based on estimates of the conditional probabilities. The method can be easily implemented with good robustness, as illustrated through theory, simulation and a data example.

Suggested Citation

  • Jing Lei, 2014. "Classification with confidence," Biometrika, Biometrika Trust, vol. 101(4), pages 755-769.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:4:p:755-769.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu038
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    Cited by:

    1. Leying Guan & Robert Tibshirani, 2022. "Prediction and outlier detection in classification problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 524-546, April.
    2. Wei Liu & Frank Bretz & Natchalee Srimaneekarn & Jianan Peng & Anthony J. Hayter, 2019. "Confidence Sets for Statistical Classification," Stats, MDPI, vol. 2(3), pages 1-15, June.

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