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An Algorithmic Approach to the Main Concepts of Rough Set Theory

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Innovations in Intelligent Systems

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 140))

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Summary

The Rough Set Theory (RST) is a mathematical formalism for representing uncertainty, which can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge based systems. This chapter introduces the main concepts of the RST and presents a family of algorithms for implementing them.

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Authors
  1. Joaquim Quinteiro Uchôa
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  2. Maria do Carmo Nicoletti
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Editor information

Editors and Affiliations

  1. Computer Science Department, Oklahoma State University, 700 N Greenwood Avenue, 74106, Tulsa, OK, USA

    Ajith Abraham

  2. Knowledge-Based Intelligent Engineering Systems Centre (KES), University of South Australia, 5095, Adelaide, Mawson Lakes, South Australia, Australia

    Lakhmi Jain

  3. Department of Electrical Engineering, University of Twente, P.O. Box 217, 7500AE, Enschede, The Netherlands

    Berend Jan van der Zwaag

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© 2004 Springer-Verlag Berlin Heidelberg

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Uchôa, J.Q., do Carmo Nicoletti, M. (2004). An Algorithmic Approach to the Main Concepts of Rough Set Theory. In: Abraham, A., Jain, L., van der Zwaag, B.J. (eds) Innovations in Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39615-4_4

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  • DOI: https://doi.org/10.1007/978-3-540-39615-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05784-7

  • Online ISBN: 978-3-540-39615-4

  • eBook Packages: Springer Book Archive

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