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Handling Uncertain Attribute Values in Decision Tree Classifier Using the Belief Function Theory

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

Decision trees are regarded as convenient machine learning techniques for solving complex classification problems. However, the major shortcoming of the standard decision tree algorithms is their unability to deal with uncertain environment. In view of this, belief decision trees have been introduced to cope with the case of uncertainty present in class’ value and represented within the belief function framework. Since in various real data applications, uncertainty may also appear in attribute values, we propose to develop in this paper another version of decision trees in a belief function context to handle the case of uncertainty present only in attribute values for both construction and classification phases.

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Author information

Authors and Affiliations

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

    Asma Trabelsi & Zied Elouedi

  2. Univ. Artois, EA 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), 62400, Béthune, France

    Eric Lefevre

Authors
  1. Asma Trabelsi
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  2. Zied Elouedi
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  3. Eric Lefevre
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Corresponding author

Correspondence to Asma Trabelsi .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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© 2016 Springer International Publishing Switzerland

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Trabelsi, A., Elouedi, Z., Lefevre, E. (2016). Handling Uncertain Attribute Values in Decision Tree Classifier Using the Belief Function Theory. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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