Multilabel classification via calibrated label ranking |
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Authors: | Johannes Fürnkranz Eyke Hüllermeier Eneldo Loza Mencía Klaus Brinker |
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Affiliation: | 1. TU Darmstadt, Darmstadt, Germany 2. Philipps-Universit?t Marburg, Marburg, Germany
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Abstract: | 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. The key idea of the approach is to introduce an artificial calibration
label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed
as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate
classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization,
image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel
learning methods. |
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Keywords: | Multi-label classification Preference learning Ranking |
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