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Controling the Number of Focal Elements

Some Combinatorial Considerations

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Belief Functions: Theory and Applications

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

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Abstract

A basic belief assignment can have up to 2n focal elements, and combining them with a simple conjunctive operator will need \({\mathcal O}(2^{2n})\) operations. This article proposes some techniques to limit the size of the focal sets of the bbas to be combined while preserving a large part of the information they carry.

The first section revisits some well-known definitions with an algorithmic point of vue. The second section proposes a matrix way of building the least committed isopignistic, and extends it to some other bodies of evidence. The third section adapts the k-means algorithm for an unsupervized clustering of the focal elements of a given bba.

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References

  1. Ball, G.H., Hall, D.J.: Isodata, a novel methed of data analysis and pattern classification. Tech. rep., Stanford Research Institute (1965)

    Google Scholar 

  2. Denoeux, T., Yaghlane, A.B.: Approximating the combination of belief functions using the fast moebius transform in a coarsened frame. International Journal of Approximate Reasoning 31(1-2), 77–101 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Garey, M.R., Johnson, D.S.: Computers and intractability – a guide to the theory of NP-Completeness. Freeman (1979)

    Google Scholar 

  4. Janez, F., Appriou, A.: Théorie de l’Evidence et cadres de discernement non exhaustifs. Traitement du Signal 13(3), 237–250 (1996)

    MATH  Google Scholar 

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

    Google Scholar 

  6. Smets, P.: Constructing the pignistic probability function in a context of uncertainty. Uncertainty in Artificial Intelligence 5, 29–39 (1990)

    Google Scholar 

  7. Smets, P.: The transferable belief model. Artificial Intelligent 66, 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  8. Janez, F., Appriou, A.: Théorie de l’Evidence et cadres de discernement non exhaustifs. Traitement du Signal 13(3), 237–250 (1996)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Christophe Osswald .

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© 2012 Springer-Verlag Berlin Heidelberg

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Osswald, C. (2012). Controling the Number of Focal Elements. 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_16

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  • DOI: https://doi.org/10.1007/978-3-642-29461-7_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29460-0

  • Online ISBN: 978-3-642-29461-7

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