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Abstract

This work focuses on label ranking, a particular task of preference learning, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels. This paper discusses and proposes alternative reduction techniques that decompose the original problem into binary classification related to pairs of labels and that can take into account label correlation during the learning process.

Keywords

Preference Learning Label Ranking Reduction Techniques Machine Learning Binary Classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Massimo Gurrieri
    • 1
  • Philippe Fortemps
    • 1
  • Xavier Siebert
    • 1
  1. 1.UMonsMonsBelgium

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