2024-04-18-aghbalou24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Sharp error bounds for imbalanced classification: how many examples in the minority class?	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 probability tending to zero. To address this gap, we present two novel contributions in the setting where the rare class probability approaches zero: (1) a non asymptotic fast rate probability bound for constrained balanced empirical risk minimization, and (2) a consistent upper bound for balanced nearest neighbors estimates. Our findings provide a clearer understanding of the benefits of class-weighting in realistic settings, opening new avenues for further research in this field.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	aghbalou24a	0	Sharp error bounds for imbalanced classification: how many examples in the minority class?	838	846	838-846	838	false	Aghbalou, Anass and Sabourin, Anne and Portier, Fran\c{c}ois	given family Anass Aghbalou given family Anne Sabourin given family François Portier	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/aghbalou24a/aghbalou24a.pdf