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Learning Structure in Evidential Networks from Evidential DataBases

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9161))

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Abstract

Evidential networks have gained a growing interest as a good tool fusing belief function theory and graph theory to analyze complex systems with uncertain data. The graphical structure of these models is not always clear, it can be fixed by experts or constructed from existing data. The main issue of this paper is how to extract the graphical structure of an evidential network from imperfect data stored in evidential databases.

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Author information

Authors and Affiliations

  1. LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Le Bardo, Tunisia

    Narjes Ben Hariz & Boutheina Ben Yaghlane

Authors
  1. Narjes Ben Hariz
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  2. Boutheina Ben Yaghlane
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Corresponding author

Correspondence to Narjes Ben Hariz .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Sébastien Destercke

  2. Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, France

    Thierry Denoeux

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Hariz, N.B., Yaghlane, B.B. (2015). Learning Structure in Evidential Networks from Evidential DataBases. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-20807-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20806-0

  • Online ISBN: 978-3-319-20807-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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