2021-03-18-bao21b.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
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation	We study class-posterior probability estimation (CPE) for binary responses where one class has much fewer data than the other. For example, events such as species co-occurrence in ecology and wars in political science are often much rarer than non-events. Logistic regression has been widely used for CPE, while it tends to underestimate the probability of rare events. Its main drawback is symmetry of the logit link—symmetric links can be misled by small and imbalanced samples because it is more incentivized to overestimate the majority class with finite samples. Parametric skewed links have been proposed to overcome this limitation, but their estimation usually results in nonconvex optimization unlike the logit link. Such nonconvexity is knotty not only from the computational viewpoint but also in terms of the parameter identifiability. In this paper, we provide a procedure to derive a convex loss for a skewed link based on the recently proposed Fenchel-Young losses. The derived losses are always convex and have a nice property suitable for class imbalance. The simulation shows the practicality of the derived losses.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bao21b	0	Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation	1648	1656	1648-1656	1648	false	Bao, Han and Sugiyama, Masashi	given family Han Bao given family Masashi Sugiyama	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/bao21b/bao21b.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/bao21b/bao21b-supp.pdf