Selective Sampling for Classification

  • François Laviolette
  • Mario Marchand
  • Sara Shanian
Conference paper

DOI: 10.1007/978-3-540-68825-9_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)
Cite this paper as:
Laviolette F., Marchand M., Shanian S. (2008) Selective Sampling for Classification. In: Bergler S. (eds) Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science, vol 5032. Springer, Berlin, Heidelberg

Abstract

Supervised learning is concerned with the task of building accurate classifiers from a set of labelled examples. However, the task of gathering a large set of labelled examples can be costly and time-consuming. Active learning algorithms try to reduce this labelling cost by performing a small number of label-queries from a large set of unlabelled examples during the process of building a classifier. However, the level of performance achieved by active learning algorithms is not always up to our expectations and no rigorous performance guarantee, in the form of a risk bound, exists for non-trivial active learning algorithms. In this paper, we propose a novel (and easy to implement) active learning algorithm having a rigorous performance guarantee (i.e., a valid risk bound) and that performs very well in comparison with some widely-used active learning algorithms.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François Laviolette
    • 1
  • Mario Marchand
    • 1
  • Sara Shanian
    • 1
  1. 1.IFT-GLOUniversité LavalCanada

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