NDLI: On empirical tradeoffs in large scale hierarchical classification

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On empirical tradeoffs in large scale hierarchical classification

Content Provider	ACM Digital Library
Author	Amblard, Cecile Gaussier, Eric Partalas, Ioannis Babbar, Rohit
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.
Starting Page	2299
Ending Page	2302
Page Count	4
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398625
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Empirical tradeoffs Hierarchical classification
Content Type	Text
Resource Type	Article

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