Skip to main content
SpringerLink
Log in

About Sources Dependence in the Theory of Belief Functions

  • Conference paper
Belief Functions: Theory and Applications

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 164))

Abstract

In the theory of belief functions many combination rules are proposed in the purpose of merging and confronting several sources opinions. Some combination rules are used when sources are cognitively independent whereas others are specific to dependent sources. In this paper, we suggest a method to quantify sources degrees of dependence in order to choose the more appropriate combination rule. We used generated mass functions to test the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ben Yaghlane, B., Smets, P., Mellouli, K.: Belief function independence: I. The marginal case. International Journal Approximate Reasoning 29(1), 47–70 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ben Yaghlane, B., Smets, P., Mellouli, K.: Belief function independence: II. The conditional case. International Journal Approximate Reasoning 31(1-2), 31–75 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ben Hariz, S., Elouedi, Z., Mellouli, K.: Clustering Approach Using Belief Function Theory. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 162–171. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Dempster, A.P.: Upper and Lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38, 325–339 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  5. Denoeux, T.: The cautious rule of combination for belief functions and some extensions. In: Proceedings of the Ninth International Conference on Information Fusion (FUSION 2006), Florence, Italy (2006)

    Google Scholar 

  6. Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4, 244–264 (1988)

    Article  Google Scholar 

  7. Martin, A., Osswald, C.: Toward a combination rule to deal with partial conflict and specificity in belief functions theory. In: Int. Conf. on Information Fusion, Québec, Canada (2007)

    Google Scholar 

  8. Jousselme, A.-L., Grenier, D., Bossé, E.: A new distance between two bodies of evidence. Information Fusion 2, 91–101 (2001)

    Article  Google Scholar 

  9. Murphy, C.K.: Combining belief functions when evidence conflicts. Decision Support Systems 29, 1–9 (2000)

    Article  Google Scholar 

  10. Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligent 66, 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  11. Smets, P.: Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem. Classic Works of the Dempster-Shafer Theory of Belief Functions, 633–664 (2008)

    Google Scholar 

  12. Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)

    Google Scholar 

  13. Yager, R.R.: On the Dempster-Shafer Framework and New Combination Rules. Information Sciences 41, 93–137 (1987)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LARODEC Laboratory, ISG Tunis, 41 Rue de la liberté, Cité Bouchoucha, 2000, Le Bardo, Tunisia

    Mouna Chebbah

  2. IRISA, University of Rennes 1, rue E. Branly, 22300, Lannion, France

    Mouna Chebbah & Arnaud Martin

  3. LARODEC Laboratory, IHEC Carthage, Carthage Présidence, 2016, Tunisia

    Boutheina Ben Yaghlane

Authors
  1. Mouna Chebbah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arnaud Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boutheina Ben Yaghlane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouna Chebbah .

Editor information

Editors and Affiliations

  1. , Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, 60205, France

    Thierry Denoeux

  2. Université de Picardie Jules Verne, Compiègne, France

    Marie-Hélène Masson

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chebbah, M., Martin, A., Ben Yaghlane, B. (2012). About Sources Dependence in the Theory of Belief Functions. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29461-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29460-0

  • Online ISBN: 978-3-642-29461-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation