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SOCO
2016
Springer

Modelling and predicting partial orders from pairwise belief functions

3 years 10 days ago
Modelling and predicting partial orders from pairwise belief functions
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.
Marie-Hélène Masson, Sébastie
Added 09 Apr 2016
Updated 09 Apr 2016
Type Journal
Year 2016
Where SOCO
Authors Marie-Hélène Masson, Sébastien Destercke, Thierry Denoeux
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