Sharp error bounds for imbalanced classification: how many examples in the minority class?

arXiv:2310.14826v2 Announce Type: replace-cross
Abstract: When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure. Despite significant theoretical work in this area, existing results do not adequately address a main challenge within the imbalanced classification framework, which is the negligible size of one class in relation to the full sample size and the need to rescale the risk function by a …

abstract arxiv class classification cs.lg data error examples function loss negative positive results risk standard stat.ml true type work