Label ranking by learning pairwise preferences

@article{Hllermeier2008LabelRB,
  title={Label ranking by learning pairwise preferences},
  author={E. H{\"u}llermeier and Johannes F{\"u}rnkranz and W. Cheng and K. Brinker},
  journal={Artif. Intell.},
  year={2008},
  volume={172},
  pages={1897-1916}
}
Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is… Expand
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