Comparative classifier evaluation for web-scale taxonomies using power law

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

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


In the context of web-scale taxonomies such as Directory Mozilla( ), previous works have shown the existence of power law distribution in the size of the categories for every level in the taxonomy. In this work, we analyse how such high-level semantics can be leveraged to evaluate accuracy of hierarchical classifiers which automatically assign the unseen documents to leaf-level categories. The proposed method offers computational advantages over k-fold cross-validation.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publicationESWC 2013 Satellite Events, Revised Selected Papers
PublisherSpringer Heidelberg
Number of pages2
ISBN (Electronic)9783642412424
ISBN (Print)9783642412417
Publication statusPublished - 26 May 2013
EventESWC 2013 Satellite Events: The Semantic Web - Montpellier, France
Duration: 26 May 201330 May 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7955 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceESWC 2013 Satellite Events: The Semantic Web

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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