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Belief Rough Set Classifier

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Advances in Artificial Intelligence (Canadian AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5549))

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Abstract

In this paper, we propose a new rough set classifier induced from partially uncertain decision system. The proposed classifier aims at simplifying the uncertain decision system and generating more significant belief decision rules for classification process. The uncertainty is reperesented by the belief functions and exists only in the decision attribute and not in condition attribute values.

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Author information

Authors and Affiliations

  1. Larodec, Institut Supérieur de Gestion de Tunis, Tunisia

    Salsabil Trabelsi & Zied Elouedi

  2. Saint Mary’s University Halifax, Canada

    Pawan Lingras

Authors
  1. Salsabil Trabelsi
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  2. Zied Elouedi
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  3. Pawan Lingras
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Editor information

Editors and Affiliations

  1. Department of Computer Science Irving K. Barber School of Arts and Sciences, University of British Columbia Okanagan, 3333 University Way, V1V 1V5, Kelowna, British Columbia, Canada

    Yong Gao

  2. School of Information Technology & Engineering, University of Ottawa, 800 King Edward Avenue, P.O. Box 450, K1N 6N5, Stn. A, Ottawa, Ontario, Canada

    Nathalie Japkowicz

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© 2009 Springer-Verlag Berlin Heidelberg

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Trabelsi, S., Elouedi, Z., Lingras, P. (2009). Belief Rough Set Classifier. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_37

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  • DOI: https://doi.org/10.1007/978-3-642-01818-3_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01817-6

  • Online ISBN: 978-3-642-01818-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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