Adaptive classifier selection in large-scale hierarchical classification

Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)


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 - 19th International Conference, ICONIP 2012, Proceedings
Number of pages8
EditionPART 3
ISBN (Electronic)9783642344879
Publication statusPublished - 12 Nov 2012
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Neural Information Processing, ICONIP 2012


  • Classifier Selection
  • Hierarchical Classification
  • Meta- learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Adaptive classifier selection in large-scale hierarchical classification'. Together they form a unique fingerprint.

Cite this