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Learning from Label Preferences

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Discovery Science (DS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6926))

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Abstract

In this paper, we review the framework of learning (from) label preferences, a particular instance of preference learning. Following an introduction to the learning setting, we particularly focus on our own work, which addresses this problem via the learning by pairwise comparison paradigm. From a machine learning point of view, learning by pairwise comparison is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We also discuss how a number of common machine learning tasks, such as multi-label classification, hierarchical classification or ordinal classification, may be addressed within the framework of learning from label preferences. We also briefly address theoretical questions as well as algorithmic and complexity issues.

Large parts of this paper are based on [18] and [19].

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Author information

Authors and Affiliations

  1. Philipps-Universität Marburg, Germany

    Eyke Hüllermeier

  2. Technische Universität Darmstadt, Germany

    Johannes Fürnkranz

Authors
  1. Eyke Hüllermeier
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  2. Johannes Fürnkranz
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Editor information

Editors and Affiliations

  1. Department of Software Systems, Tampere University of Technology, P. O. Box 553, 33101, Tampere, Finland

    Tapio Elomaa

  2. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Jaakko Hollmén

  3. Helsinki Institute for Information Technology (HIIT), Finland

    Heikki Mannila

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Hüllermeier, E., Fürnkranz, J. (2011). Learning from Label Preferences. In: Elomaa, T., Hollmén, J., Mannila, H. (eds) Discovery Science. DS 2011. Lecture Notes in Computer Science(), vol 6926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24477-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-24477-3_2

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