Adaptive Classifier Selection in Large-scale Hierarchical Classification

Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard

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

Abstract

Going beyond the traditional text classification, involving a few tens of classes, there has been a surge of interest in automatic document categorization in large taxonomies where the number of classes range from hundreds of thousands to millions. Due to the complex nature of the learning problem posed in such scenarios, one needs to adapt the conventional classification schemes to suit this domain. This paper presents a novel approach for classifier selection in large hierarchies, which is based on exploiting training data heterogeneity across the hierarchy. We also present a meta-learning framework for further flexibility in classifier selection. The experimental results demonstrate the applicability of our approach, which achieves accuracy comparable to the state-of-the-art and is also significantly faster for prediction.
Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication ICONIP 2012. Lecture Notes in Computer Science
Pages612-619
Number of pages8
Volume7665
ISBN (Electronic)978-3-642-34487-9
DOIs
Publication statusPublished - 2012
MoE publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

ConferenceINTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING
Abbreviated titleICONIP
CountryQatar
CityDoha
Period12/11/201215/11/2012

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