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Basic Properties for Total Uncertainty Measures in the Theory of Evidence

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Information Quality in Information Fusion and Decision Making

Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

The theory of evidence is a generalization of the probability theory which has been used in many applications. That generalization permits to represent more different types of uncertainty. To quantify the total information uncertainty in theory of evidence, several measures have been proposed in the last decades. From the axiomatic point of view, any uncertainty measure must verify a set of important properties to guarantee a correct behavior. Thus, a total measure in theory of evidence must preserve the total amount of information or not to decrease when uncertainty is increased. In this chapter we review and revise the properties of a total measure of uncertainty considered in the literature.

The work of the first two authors has been supported by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Joaquín Abellán & Carlos J. Mantas

  2. IMT-Atlantique, Brest, France

    Éloi Bossé

  3. McMaster University, Hamilton, Canada

    Éloi Bossé

Authors
  1. Joaquín Abellán
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  2. Carlos J. Mantas
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  3. Éloi Bossé
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Corresponding author

Correspondence to Joaquín Abellán .

Editor information

Editors and Affiliations

  1. IMT-Atlantique, Brest, France

    Éloi Bossé

  2. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

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Abellán, J., Mantas, C.J., Bossé, É. (2019). Basic Properties for Total Uncertainty Measures in the Theory of Evidence. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-03643-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03642-3

  • Online ISBN: 978-3-030-03643-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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