Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification

  • Rohit Babbar
  • Ioannis Partalas
  • Eric Gaussier
  • Massih-Reza Amini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

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.

Keywords

Support Vector Machine Target Class Decision Node Hierarchy Structure Prediction Speed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bennett, P.N., Nguyen, N.: Refined experts: improving classification in large taxonomies. In: Proc. 32nd Int’l ACM SIGIR, pp. 11–18. ACM (2009)Google Scholar
  2. 2.
    Cai, L., Hofmann, T.: Hierarchical document categorization with support vector machines. In: CIKM, pp. 78–87. ACM (2004)Google Scholar
  3. 3.
    Dekel, O.: Distribution-calibrated hierarchical classification. In: Advances in Neural Information Processing Systems, pp. 450–458 (2009)Google Scholar
  4. 4.
    Dekel, O., Keshet, J., Singer, Y.: Large margin hierarchical classification. In: Proceedings of the 21st International Conference on Machine Learning, ICML 2004, pp. 27–34 (2004)Google Scholar
  5. 5.
    Gao, T., Koller, D.: Discriminative learning of relaxed hierarchy for large-scale visual recognition. In: IEEE International Conference on Computer Vision (ICCV), pp. 2072–2079 (2011)Google Scholar
  6. 6.
    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. SIGKDD Explor. Newsl., 36–43 (2005)Google Scholar
  7. 7.
    Malik, H.: Improving hierarchical svms by hierarchy flattening and lazy classification. In: 1st Pascal Workshop on Large Scale Hierarchical Classification (2009)Google Scholar
  8. 8.
    Partalas, I., Babbar, R., Gaussier, E., Amblard, C.: Adaptive classifier selection in large-scale hierarchical classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 612–619. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Lu, B.-L.: Flatten hierarchies for large-scale hierarchical text categorization. In: Fifth IEEE International Conference on Digital Information Management, pp. 139–144 (2010)Google Scholar
  10. 10.
    Xue, G.-R., Xing, D., Yang, Q., Yu, Y.: Deep classification in large-scale text hierarchies. In: Proc. 31st Int’l ACM SIGIR, pp. 619–626. ACMGoogle Scholar
  11. 11.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd International ACM SIGIR, pp. 42–49. ACM (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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