2024-04-18-cao24a.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
Consistent Hierarchical Classification with A Generalized Metric	In multi-class hierarchical classification, a natural evaluation metric is the tree distance loss that takes the value of two labels’ distance on the pre-defined tree hierarchy. This metric is motivated by that its Bayes optimal solution is the deepest label on the tree whose induced superclass (subtree rooted at it) includes the true label with probability at least $\frac{1}{2}$. However, it can hardly handle the risk sensitivity of different tasks since its accuracy requirement for induced superclasses is fixed at $\frac{1}{2}$. In this paper, we first introduce a new evaluation metric that generalizes the tree distance loss, whose solution’s accuracy constraint $\frac{1+c}{2}$ can be controlled by a penalty value $c$ tailored for different tasks: a higher c indicates the emphasis on prediction’s accuracy and a lower one indicates that on specificity. Then, we propose a novel class of consistent surrogate losses based on an intuitive presentation of our generalized metric and its regret, which can be compatible with various binary losses. Finally, we theoretically derive the regret transfer bounds for our proposed surrogates and empirically validate their usefulness on benchmark datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cao24a	0	Consistent Hierarchical Classification with A Generalized Metric	4825	4833	4825-4833	4825	false	Cao, Yuzhou and Feng, Lei and An, Bo	given family Yuzhou Cao given family Lei Feng given family Bo An	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/cao24a/cao24a.pdf