mlsanityData to 7 Oct 2026

Classification with Rejection Based on Cost-sensitive Classification

arXiv 2010.11748 ↗First posted 22 Oct 2020stat.ML cs.LGcited by 55

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Abstract (arXiv)

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of cost-sensitive classifiers, which satisfies all the following properties: (i) it can avoid estimating class-posterior probabilities, resulting in improved classification accuracy, (ii) it allows a flexible choice of losses including non-convex ones, (iii) it does not require complicated modifications when using different losses, (iv) it is applicable to both binary and multiclass cases, and (v) it is theoretically justifiable for any classification-calibrated loss. Experimental results demonstrate the usefulness of our proposed approach in clean-labeled, noisy-labeled, and positive-unlabeled classification.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 17; 2024: 12; 2025: 6; 2026: 9.

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  1. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  2. 2021Machine Learning with a Reject Option: A surveycited by 107
  3. 2022Calibrated Learning to Defer with One-vs-All Classifierscited by 68
  4. 2023Who Should Predict? Exact Algorithms For Learning to Defer to Humanscited by 68
  5. 2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62
  6. 2022Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensemblescited by 59
  7. 2023When Does Confidence-Based Cascade Deferral Suffice?cited by 58
  8. 2022Sample Efficient Learning of Predictors that Complement Humanscited by 46
  9. 2023Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithmscited by 36
  10. 2023Learning to Reject with a Fixed Predictor: Application to Decontextualizationcited by 32
  11. 2022Calibrated Selective Classificationcited by 30
  12. 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defercited by 26
38 more
  1. 2024Learning to Defer to a Population: A Meta-Learning Approachcited by 26
  2. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  3. 2024A Universal Growth Rate for Learning with Smooth Surrogate Lossescited by 24
  4. 2022Towards Better Selective Classificationcited by 21
  5. 2025Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defercited by 16
  6. 2021Surrogate Regret Bounds for Polyhedral Lossescited by 15
  7. 2023Plugin estimators for selective classification with out-of-distribution detectioncited by 14
  8. 2023Regression with Cost-based Rejectioncited by 11
  9. 2025Budgeted Multiple-Expert Deferralcited by 11
  10. 2023Unsupervised Anomaly Detection with Rejectioncited by 9
  11. 2023Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edgecited by 5
  12. 2023Unified Risk Analysis for Weakly Supervised Learningcited by 5
  13. 2023Learning to Complement with Multiple Humanscited by 5
  14. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  15. 2023Stratified Adversarial Robustness with Rejectioncited by 4
  16. 2023Online Decision Mediationcited by 4
  17. 2024Classification with Conceptual Safeguardscited by 4
  18. 2025Learning Conservative Neural Control Barrier Functions from Offline Datacited by 4
  19. 2023On the Fly Neural Style Smoothing for Risk-Averse Domain Generalizationcited by 3
  20. 2024Partial-Label Learning with a Reject Optioncited by 3
  21. 2024Efficient Online Set-valued Classification with Bandit Feedbackcited by 3
  22. 2024Rejection via Learning Density Ratioscited by 3
  23. 2024Learning Causal Transition Matrix for Instance-dependent Label Noisecited by 3
  24. 2025Estimating Control Barriers from Offline Datacited by 3
  25. 2023Conformalized Semi-supervised Random Forest for Classification and Abnormality Detectioncited by 2
  26. 2024Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Scorecited by 2
  27. 2024MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learningcited by 2
  28. 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defercited by 2
  29. 2022Rethinking and Recomputing the Value of Machine Learning Modelscited by 1
  30. 2023Deep Neural Networks Tend To Extrapolate Predictablycited by 1
  31. 2024Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learningcited by 1
  32. 2025Randomized multi-class classification under system constraints: a unified approach via post-processingcited by 1
  33. 2026Density-Ratio Losses for Post-Hoc Learning to Defercited by 1
  34. 2026Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluationcited by 1
  35. 2026Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmarkcited by 1
  36. 2021A Symmetric Loss Perspective of Reliable Machine Learningcited by 0
  37. 2023Using Reed-Muller Codes for Classification with Rejection and Recoverycited by 0
  38. 2025Direct Preference Optimization for Adaptive Concept-based Explanationscited by 0

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