Modelling and predicting partial orders from pairwise belief functions |
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Authors: | Marie-Hélène Masson Sébastien Destercke Thierry Denoeux |
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Affiliation: | 1.Université de Picardie Jules Verne,Amiens,France;2.UMR CNRS 7253 Heudiasyc,Université de Technologie de Compiègne,Compiègne Cedex,France |
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Abstract: | In this paper, we introduce a generic way to represent and manipulate pairwise information about partial orders (representing rankings, preferences, ...) with belief functions. We provide generic and practical tools to make inferences from this pairwise information and illustrate their use on the machine learning problems that are label ranking and multi-label prediction. Our approach differs from most other quantitative approaches handling complete or partial orders, in the sense that partial orders are here considered as primary objects and not as incomplete specifications of ideal but unknown complete orders. |
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