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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Notes
\((\lambda _i, \lambda _j),(\lambda _j,\lambda _k) \in R \Rightarrow (\lambda _i,\lambda _k) \in R\).
The data sets are available at http://www.uni-marburg.de/fb12/kebi/research/repository/labelrankingdata.
The software can be downloaded from https://www.hds.utc.fr/~tdenoeux/.
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Acknowledgments
This work was carried out in the framework of the Labex MS2T, which was funded by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).
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Communicated by V. Loia.
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Masson, MH., Destercke, S. & Denoeux, T. Modelling and predicting partial orders from pairwise belief functions. Soft Comput 20, 939–950 (2016). https://doi.org/10.1007/s00500-014-1553-9
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DOI: https://doi.org/10.1007/s00500-014-1553-9