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Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law

  • Rohit Babbar
  • Ioannis Partalas
  • Cornelia Metzig
  • Eric Gaussier
  • Massih-reza Amini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)

Abstract

In the context of web-scale taxonomies such as Directory Mozilla( www.dmoz.org ), 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.

References

  1. 1.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: SIGCOMMGoogle Scholar
  2. 2.
    Liu, T.-Y., Yang, Y., Wan, H., Zeng, H.-J., Chen, Z., Ma, W.-Y.: Support vector machines classification with a very large-scale taxonomy. In: SIGKDD (2005)Google Scholar
  3. 3.
    Newman, M.E.J.: Power laws, Pareto distributions and Zipf’s law. Contemporary Physics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rohit Babbar
    • 1
  • Ioannis Partalas
    • 1
  • Cornelia Metzig
    • 1
  • Eric Gaussier
    • 1
  • Massih-reza Amini
    • 1
  1. 1.Laboratoire d’Informatique de GrenobleUniversité Joseph FourierGrenobleFrance

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