Label ranking by learning pairwise preferences

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 s...

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Bibliographic Details
Published in:Artificial intelligence 2008-11, Vol.172 (16), p.1897-1916
Main Authors: Hüllermeier, Eyke, Fürnkranz, Johannes, Cheng, Weiwei, Brinker, Klaus
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Constraint classification
Exact sciences and technology
Learning and adaptive systems
Pairwise classification
Preference learning
Ranking
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Summary: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 then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2008.08.002