  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.
