A Selective Sampling Strategy for Label Ranking

  • Massih Amini
  • Nicolas Usunier
  • François Laviolette
  • Alexandre Lacasse
  • Patrick Gallinari
Conference paper

DOI: 10.1007/11871842_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)
Cite this paper as:
Amini M., Usunier N., Laviolette F., Lacasse A., Gallinari P. (2006) A Selective Sampling Strategy for Label Ranking. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, vol 4212. Springer, Berlin, Heidelberg

Abstract

We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen’s generalization bounds using unlabeled data [7], initially developed in the context of classification. The bounds we obtain suggest a selective sampling strategy provided that a sufficiently, yet reasonably large initial labeled dataset is provided. Experiments on Information Retrieval corpora from automatic text summarization and question/answering show that the proposed approach allows to substantially reduce the labeling effort in comparison to random and heuristic-based sampling strategies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Massih Amini
    • 1
  • Nicolas Usunier
    • 1
  • François Laviolette
    • 2
  • Alexandre Lacasse
    • 2
  • Patrick Gallinari
    • 1
  1. 1.Laboratoire d’Informatique de Paris 6Université Pierre et Marie CurieParisFrance
  2. 2.Département IFT-GLOUniversité LavalSainte-Foy (QC)Canada

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