Inducing a hierarchy for multi-class classification problems

• URL: http://arxiv.org/abs/2102.10263v1
• Date: Sat, 20 Feb 2021 05:40:42 GMT
• Title: Inducing a hierarchy for multi-class classification problems
• Authors: Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher White, Ali Geisa, Carey E. Priebe
• Abstract summary: In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. In this paper, we investigate a class of methods that induce a hierarchy that can similarly improve classification performance over flat classifiers. 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.
• Score: 11.58041597483471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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