On Empirical Tradeoffs in Large Scale Hierarchical Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Cecile Amblard

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

While multi-class categorization of documents has been of research interest for over a decade, relatively fewer approaches have been proposed for large scale taxonomies in which the number of classes range from hundreds of thousand as in Directory Mozilla to over a million in Wikipedia. As a result of ever increasing number of text documents and images from various sources, there is an immense need for automatic classification of documents in such large hierarchies. In this paper, we analyze the tradeoffs between the important characteristics of different classifiers employed in the top down fashion. The properties for relative comparison of these classifiers include, (i) accuracy on test instance, (ii) training time (iii) size of the model and (iv) test time required for prediction. Our analysis is motivated by the well known error bounds from learning theory, which is also further reinforced by the empirical observations on the publicly available data from the Large Scale Hierarchical Text Classification Challenge. We show that by exploiting the data heterogenity across the large scale hierarchies, one can build an overall classification system which is approximately 4 times faster for prediction, 3 times faster to train, while sacrificing only 1% point in accuracy.
Original languageEnglish
Title of host publicationCIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
PublisherACM
Pages2299-2302
Number of pages3
ISBN (Print)978-1-4503-1156-4
DOIs
Publication statusPublished - 29 Oct 2012
MoE publication typeA4 Article in a conference publication
EventACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT - Maui, United States
Duration: 29 Oct 20122 Nov 2012

Conference

ConferenceACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Abbreviated titleCIKM
CountryUnited States
CityMaui
Period29/10/201202/11/2012

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