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Pairwise Preference Learning and Ranking

  • Johannes Fürnkranz
  • Eyke Hüllermeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a ranking function by reducing the original problem to a number of binary classification problems, one for each pair of labels. The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example. To this end, we present theoretical results on the complexity of pairwise preference learning, and experimentally investigate the predictive performance of our method for different types of preference information, such as top-ranked labels and complete rankings. The domain of this study is the prediction of a rational agent’s ranking of actions in an uncertain environment.

Keywords

Ranking Function Total Order Preference Information Round Robin Expected Utility Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Johannes Fürnkranz
    • 1
  • Eyke Hüllermeier
    • 2
  1. 1.Austrian Research Institute for Artificial IntelligenceWienAustria
  2. 2.Informatics InstituteMarburg UniversityMarburgGermany

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