Alper Uygun

Selective classification under imbalance in multiclass settings: A novel metric for bias-aware risk-coverage evaluation

OBJECTIVE Selective classification improves reliability by allowing models to abstain on uncertain inputs, which is critical in safety-sensitive domains such as healthcare. However, commonly used evaluation metrics obscure fairness issues under class imbalance, leading to disproportionately high rejection of minority c...