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Communication dans un congrès

Continuous belief functions and α-stable distributions

Anthony Fiche 1 Arnaud Martin Jean-Christophe Cexus 2 Ali Khenchaf 1
1 Lab-STICC_ENSTAB_MOM_PIM ; REMS
E3I2 - Extraction et Exploitation de l'Information en Environnements Incertains, STIC - Pôle STIC [Brest]
2 Lab-STICC_ENSTAB_CID_TOMS; REMS
STIC - Pôle STIC [Brest], Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The theory of belief functions has been formalized in continuous domain for pattern recognition. Some applications use assumption of Gaussian models. However, this assumption is reductive. Indeed, some data are not symmetric and present property of heavy tails. It is possible to solve these problems by using a class of distributions called α-stable distributions. Consequently, we present in this paper a way to calculate pignistic probabilities with plausibility functions where the knowledge of the sources of information is represented by symmetric α-stable distributions. To validate our approach, we compare our results in special case of Gaussian distributions with existing methods. To illustrate our work, we generate arbitrary distributions which represents speed of planes and take decisions. A comparison with a Bayesian approach is made to show the interest of the theory of belief functions.
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Communication dans un congrès
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-01241525
Contributeur : Jean-Christophe Cexus <>
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Dernière modification le : mercredi 24 juin 2020 - 16:19:24
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  • HAL Id : hal-01241525, version 1

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Anthony Fiche, Arnaud Martin, Jean-Christophe Cexus, Ali Khenchaf. Continuous belief functions and α-stable distributions. Information Fusion (FUSION), 2010 13th Conference on, Jul 2010, Edinburgh, United Kingdom. 7 p. ⟨hal-01241525⟩

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