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Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4724))

Abstract

This paper addresses the issue of measuring similarity between pieces of uncertain information in the framework of possibility theory. In a first part, natural properties of such functions are proposed and a survey of the few existing measures is presented. Then, a new measure so-called Information Affinity is proposed to overcome the limits of the existing ones. The proposed function is based on two measures, namely, a classical informative distance, e.g. Manhattan distance which evaluates the difference, degree by degree, between two normalized possibility distributions and the well known inconsistency measure which assesses the conflict between the two possibility distributions. Some potential applications of the proposed measure are also mentioned in this paper.

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Authors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion de Tunis, Tunisia

    Ilyes Jenhani, Nahla Ben Amor, Zied Elouedi & Khaled Mellouli

  2. CRIL, Université d’Artois, Lens, France

    Salem Benferhat

Authors
  1. Ilyes Jenhani
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  2. Nahla Ben Amor
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  3. Zied Elouedi
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  4. Salem Benferhat
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  5. Khaled Mellouli
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Editor information

Editors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion de Tunis, 41 Avenue de la liberté, 2000, Le Bardo, Tunisie

    Khaled Mellouli

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© 2007 Springer-Verlag Berlin Heidelberg

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Jenhani, I., Ben Amor, N., Elouedi, Z., Benferhat, S., Mellouli, K. (2007). Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_73

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  • DOI: https://doi.org/10.1007/978-3-540-75256-1_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75255-4

  • Online ISBN: 978-3-540-75256-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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