Local data augmentation for imbalanced binary classification.

arXiv:2610.26418v1 Announce Type: new Abstract: Facing imbalanced binary classification, we aim at maximizing the recall while limiting the proportion of false detections in the minority class. For doing that, the present work studies the effect of several reb