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Classifying Scenarios using Belief Decision Trees

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Discovery Science (DS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1967))

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Abstract

In this paper, we propose a method based on the belief decision tree approach, to classify scenarios in an uncertain context. Our method uses both the decision tree technique and the belief function theory as understood in the transferable belief model in order to find the classes of the scenarios (of a given problem) that may happen in the future. Two major phases will be ensured: the construction of the belief decision tree representing the scenarios belonging to the training set and which may present some uncertainty in their class membership, this uncertainty is presented by belief functions. Then, the classification of new scenarios characterized generally by uncertain hypotheses’ configurations.

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Author information

Authors and Affiliations

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

    Zied Elouedi & Khaled Mellouli

Authors
  1. Zied Elouedi
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  2. Khaled Mellouli
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Editor information

Editors and Affiliations

  1. Faculty of Information Science and Electrical Engineering, Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Setsuo Arikawa

  2. Faculty of Science Department of Information Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan

    Shinichi Morishita

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© 2000 Springer-Verlag Berlin Heidelberg

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Elouedi, Z., Mellouli, K. (2000). Classifying Scenarios using Belief Decision Trees. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_11

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  • DOI: https://doi.org/10.1007/3-540-44418-1_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41352-3

  • Online ISBN: 978-3-540-44418-3

  • eBook Packages: Springer Book Archive

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