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ECAI
2006
Springer

A Unified Model for Multilabel Classification and Ranking

8 years 7 months ago
A Unified Model for Multilabel Classification and Ranking
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. We present empirical results in the area of text categorization and gene analysis, underscoring the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.
Klaus Brinker, Johannes Fürnkranz, Eyke H&uum
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ECAI
Authors Klaus Brinker, Johannes Fürnkranz, Eyke Hüllermeier
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