Learning from Ambiguously Labeled Examples

  • Eyke Hüllermeier
  • Jürgen Beringer
Conference paper

DOI: 10.1007/11552253_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)
Cite this paper as:
Hüllermeier E., Beringer J. (2005) Learning from Ambiguously Labeled Examples. In: Famili A.F., Kok J.N., Peña J.M., Siebes A., Feelders A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg

Abstract

Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eyke Hüllermeier
    • 1
  • Jürgen Beringer
    • 1
  1. 1.Fakultät für InformatikOtto-von-Guericke-Universität MagdeburgGermany

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