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Quality of Information Sources in Information Fusion

  • Frédéric PichonEmail author
  • Didier Dubois
  • Thierry Denœux
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

Pieces of information can only be evaluated if knowledge about the quality of the sources of information is available. Typically, this knowledge pertains to the source relevance. In this chapter, other facets of source quality are considered, leading to a general approach to information correction and fusion for belief functions. In particular, the case where sources may partially lack truthfulness is deeply investigated. As a result, Shafer’s discounting operation and the unnormalised Dempster’s rule, which deal only with source relevance, are considerably extended. Most notably, the unnormalised Dempster’s rule is generalised to all Boolean connectives. The proposed approach also subsumes other important correction and fusion schemes, such as contextual discounting and Smets’ α-junctions. We also study the case where pieces of knowledge about the quality of the sources are independent. Finally, some means to obtain knowledge about source quality are reviewed.

Keywords

Dempster-Shafer theory Belief functions Evidence theory Boolean logic Information fusion Discounting 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Frédéric Pichon
    • 1
    Email author
  • Didier Dubois
    • 2
  • Thierry Denœux
    • 3
  1. 1.EA 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A)Université d’ArtoisBéthuneFrance
  2. 2.CNRS, IRIT (UMR 5505)Université Paul SabatierToulouse Cedex 09France
  3. 3.CNRS, Heudiasyc (UMR 7253)Sorbonne Universités, Université de Technologie de CompiègneCompiègne CedexFrance

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