mondrian-conformal-prediction-8fc445b4·2 events·first seen Aliases: Mondrian Conformal Prediction
A new arXiv preprint benchmarks cost-sensitive conformal prediction methods across 15 real-world imbalanced tabular datasets, 7 classifiers, and 3 calibration techniques (3,150 total runs), finding that standard marginal conformal prediction drops minority-class coverage to as low as 0.5%. Class-conditional (Mondrian) conformal prediction recovers valid minority coverage with an average 61.7 percentage-point improvement, and combining it with cost-controlled abstention reduces expected decision cost under realistic human review budgets. The paper also derives dataset-specific break-even thresholds for when deferring to human experts becomes cost-effective, offering practical deployment guidance for credit scoring, fraud detection, healthcare, and industrial safety applications.
This paper proposes a hybrid selective classification framework for clinical NLP that explicitly handles both aleatoric and epistemic uncertainty to avoid overconfident predictions in medical triage settings. The system combines Mondrian conformal prediction with a Multi-Centroid Mahalanobis Distance veto, evaluated on HIV suspicion identification in Spanish clinical notes. The authors demonstrate that standard uncertainty metrics and baseline classifiers suffer coverage collapse under strict reliability constraints, while their dual-verification approach isolates a trustworthy operational domain. The work critiques inflated benchmark metrics that arise from forcing deterministic classification on inherently ambiguous clinical instances.