Maximum-margin framework for training data synchronization in large-scale hierarchical classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih Reza Amini

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

8 Citations (SciVal)


In the context of supervised learning, the training data for large-scale hierarchical classification consist of (i) a set of input-output pairs, and (ii) a hierarchy structure defining parent-child relation among class labels. It is often the case that the hierarchy structure given a-priori is not optimal for achieving high classification accuracy. This is especially true for web-taxonomies such as Yahoo! directory which consist of tens of thousand of classes. Furthermore, an important goal of hierarchy design is to render better navigability and browsing. In this work, we propose a maximum-margin framework for automatically adapting the given hierarchy by using the set of input-output pairs to yield a new hierarchy. The proposed method is not only theoretically justified but also provides a more principled approach for hierarchy flattening techniques proposed earlier, which are ad-hoc and empirical in nature. The empirical results on publicly available large-scale datasets demonstrate that classification with new hierarchy leads to better or comparable generalization performance than the hierarchy flattening techniques.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication20th International Conference, ICONIP 2013, Proceedings
PublisherSpringer Verlag
Number of pages8
EditionPART 1
ISBN (Electronic)9783642420542
ISBN (Print)9783642420535
Publication statusPublished - 3 Nov 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of

Bibliographical note

Funding details:
This work was partly supported by the French National Research Agency (ANR), Class-Y project (ANR-10-BLAN-0211), and by the European Commission (EC), BioASQ project (FP7/2007-2013, ICT-2011.4.4(d)).

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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