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On Flat versus Hierarchical Classification in Large-Scale Taxonomies
Rohit Babbar · Ioannis Partalas · Eric Gaussier · Massih R. Amini

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

We study in this paper flat and hierarchical classification strategies in the context of large-scale taxonomies. To this end, we first propose a multiclass, hierarchical data dependent bound on the generalization error of classifiers deployed in large-scale taxonomies. This bound provides an explanation to several empirical results reported in the literature, related to the performance of flat and hierarchical classifiers. We then introduce another type of bounds targeting the approximation error of a family of classifiers, and derive from it features used in a meta-classifier to decide which nodes to prune (or flatten) in a large-scale taxonomy. We finally illustrate the theoretical developments through several experiments conducted on two widely used taxonomies.

Author Information

Rohit Babbar (Université Joseph Fourier, Grenoble)
Ioannis Partalas (UJF/LIG)
Eric Gaussier (Université Joseph Fourier, Grenoble)
Massih R. Amini (University Grenoble Alps)

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