Neyman-Pearson multiclass classification under label noise via empirical likelihood
arXiv:2603.21623v2 Announce Type: replace-cross
Abstract: In many classification problems, misclassification costs are highly asymmetric, while training labels are often corrupted due to measurement error, annotator variability, or adversarial noise. …