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Soft Computing
Erschienen in: Soft Computing 3/2016

14.12.2014 | Methodologies and Application

Modelling and predicting partial orders from pairwise belief functions

verfasst von: Marie-Hélène Masson, Sébastien Destercke, Thierry Denoeux

Erschienen in: Soft Computing | Ausgabe 3/2016

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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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Fußnoten
1
\((\lambda _i, \lambda _j),(\lambda _j,\lambda _k) \in R \Rightarrow (\lambda _i,\lambda _k) \in R\).
 
3
The software can be downloaded from https://​www.​hds.​utc.​fr/​~tdenoeux/​.
 
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Metadaten
Titel
Modelling and predicting partial orders from pairwise belief functions
verfasst von
Marie-Hélène Masson
Sébastien Destercke
Thierry Denoeux
Publikationsdatum
14.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 3/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1553-9

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