@article{helm2021inducing,
author = {Helm, Hayden S. and Geisa, Ali and Yang, Weiwei and Lytvynets, Kate and Riva, Oriana and Bharadwaj, Sujeeth and Priebe, Carey E. and White, Chris},
title = {Inducing a hierarchy for multi-class classification problems},
year = {2021},
month = {February},
abstract = {In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. Un-fortunately, the majority of classification datasets do not come pre-equipped with a hierarchical structure and classical flat classifiers must be employed. In this paper, we investigate a class of methods that induce a hierarchy that can similarly improve classification performance over flat classifiers. The class of methods follows the structure of first clustering the conditional distributions and subsequently using a hierarchical classifier with the induced hierarchy. We demonstrate the effectiveness of the class of methods both for discovering a latent hierarchy and for improving accuracy in principled simulation settings and three real data applications.},
url = {http://approjects.co.za/?big=en-us/research/publication/inducing-a-hierarchy-for-multi-class-classification-problems/},
journal = {Arxiv},
volume = {2102.10263},
}