mlsanityData to 14 Oct 2026

AUC-based Selective Classification

arXiv 2210.10703 ↗First posted 19 Oct 2022cs.LG cs.AIcited by 13

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Abstract (arXiv)

Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 10 of its 42 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

10 of 42 references.

References · 10

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2017Selective Classification for Deep Neural Networkscited by 852
  3. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  4. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  5. 2018Scalable and accurate deep learning for electronic health recordscited by 280
  6. 2020Self-Adaptive Training: beyond Empirical Risk Minimizationcited by 126
  7. 2021Machine Learning with a Reject Option: A surveycited by 109
  8. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  9. 2019Deep Gamblers: Learning to Abstain with Portfolio Theorycited by 71
  10. 2020Selective Classification Can Magnify Disparities Across Groupscited by 36

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 4.

13 papers.

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  1. 2024Overcoming Common Flaws in the Evaluation of Selective Classification Systemscited by 37
  2. 2024A Causal Framework for Evaluating Deferring Systemscited by 13
  3. 2025Selective Conformal Risk Controlcited by 11
  4. 2023Unsupervised Anomaly Detection with Rejectioncited by 9
  5. 2025Interpretable and Fair Mechanisms for Abstaining Classifierscited by 2
  6. 2025Knowing What You Cannot Explain: Learning to Reject Low-Quality Explanationscited by 2
  7. 2025What Does It Take to Build a Performant Selective Classifier?cited by 2
  8. 2026Density-Ratio Losses for Post-Hoc Learning to Defercited by 1
  9. 2023Model Agnostic Explainable Selective Regression via Uncertainty Estimationcited by 0
  10. 2025Bounded-Abstention Pairwise Learning to Rankcited by 0
  11. 2026Bounded-Abstention Multi-horizon Time-series Forecastingcited by 0
  12. 2026A Regime Theory of Controller Class Selection for LLM Action Decisionscited by 0
1 more
  1. 2026Improving Selective Classification with Pairwise Queries for Binary Classificationcited by 0