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Induction of Decision Rules and Classification in the Valued Tolerance Approach

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Rough Sets and Current Trends in Computing (RSCTC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2475))

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Abstract

The problem of uncertain and/or incomplete information in information tables is addressed in the paper, mainly as far as the induction of classification rules is concerned. Two rule induction algorithms are introduced, discussed and tested on a number of benchmark data sets. Moreover, two different strategies for classifying objects on the basis of induced rules are introduced.

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References

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Jerzy Stefanowski

  2. LAMSADE - CNRS, Université Paris Dauphine, 75775, Paris Cedex 16, France

    Alexis Tsoukiás

Authors
  1. Jerzy Stefanowski
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  2. Alexis Tsoukiás
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Editor information

Editors and Affiliations

  1. Penn State Great Valley School of Professional Graduate Studies, 30 East Swedesford Road, Malvern, PA, 19355, USA

    James J. Alpigini

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Systems and Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong

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© 2002 Springer-Verlag Berlin Heidelberg

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Stefanowski, J., Tsoukiás, A. (2002). Induction of Decision Rules and Classification in the Valued Tolerance Approach. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_35

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  • DOI: https://doi.org/10.1007/3-540-45813-1_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44274-5

  • Online ISBN: 978-3-540-45813-5

  • eBook Packages: Springer Book Archive

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