Abstract
This work focuses on label ranking, a particular task of preference learning, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels. This paper discusses and proposes alternative reduction techniques that decompose the original problem into binary classification related to pairs of labels and that can take into account label correlation during the learning process.
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Gurrieri, M., Fortemps, P., Siebert, X. (2014). Alternative Decomposition Techniques for Label Ranking. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-319-08855-6_47
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DOI: https://doi.org/10.1007/978-3-319-08855-6_47
Publisher Name: Springer, Cham
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