# AUC-based Selective Classification

arXiv: https://arxiv.org/abs/2210.10703 · HTML: https://mlsanity.com/paper/2210.10703/

## 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 (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [SelectiveNet: A Deep Neural Network with an Integrated Reject Option](https://mlsanity.com/paper/1901.09192/) (2019)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Self-Adaptive Training: beyond Empirical Risk Minimization](https://mlsanity.com/paper/2002.10319/) (2020)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [Deep Gamblers: Learning to Abstain with Portfolio Theory](https://mlsanity.com/paper/1907.00208/) (2019)
- [Selective Classification Can Magnify Disparities Across Groups](https://mlsanity.com/paper/2010.14134/) (2020)

## Built on by (most cited first, 13 of 13)

- [Overcoming Common Flaws in the Evaluation of Selective Classification Systems](https://mlsanity.com/paper/2407.01032/) (2024)
- [A Causal Framework for Evaluating Deferring Systems](https://mlsanity.com/paper/2405.18902/) (2024)
- [Selective Conformal Risk Control](https://mlsanity.com/paper/2512.12844/) (2025)
- [Unsupervised Anomaly Detection with Rejection](https://mlsanity.com/paper/2305.13189/) (2023)
- [Interpretable and Fair Mechanisms for Abstaining Classifiers](https://mlsanity.com/paper/2503.18826/) (2025)
- [Knowing What You Cannot Explain: Learning to Reject Low-Quality Explanations](https://mlsanity.com/paper/2507.12900/) (2025)
- [What Does It Take to Build a Performant Selective Classifier?](https://mlsanity.com/paper/2510.20242/) (2025)
- [Density-Ratio Losses for Post-Hoc Learning to Defer](https://mlsanity.com/paper/2605.19557/) (2026)
- [Model Agnostic Explainable Selective Regression via Uncertainty Estimation](https://mlsanity.com/paper/2311.09145/) (2023)
- [Bounded-Abstention Pairwise Learning to Rank](https://mlsanity.com/paper/2505.23437/) (2025)
- [Bounded-Abstention Multi-horizon Time-series Forecasting](https://mlsanity.com/paper/2602.04714/) (2026)
- [A Regime Theory of Controller Class Selection for LLM Action Decisions](https://mlsanity.com/paper/2605.06339/) (2026)
- [Improving Selective Classification with Pairwise Queries for Binary Classification](https://mlsanity.com/paper/2605.30615/) (2026)

## 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.