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Approximations and Classifiers

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Rough Sets and Current Trends in Computing (RSCTC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6086))

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Abstract

We discuss some important issues for applications that are related to generalizations of the 1994 approximation space definition [11]. In particular, we present examples of rough set based strategies for extension of approximation spaces from samples of objects onto the whole universe of objects. This makes it possible to present methods for inducing approximations of concepts or classifications analogously to the approaches for inducing classifiers known in machine learning or data mining.

The research has been partially supported by the grants N N516 077837, N N516 069235, and N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland.

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Author information

Authors and Affiliations

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

    Andrzej Skowron

  2. Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351, Białystok, Poland

    Jarosław Stepaniuk

Authors
  1. Andrzej Skowron
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  2. Jarosław Stepaniuk
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Editor information

Editors and Affiliations

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

    Marcin Szczuka

  2. ICS, Warsaw University of Technology,,  

    Marzena Kryszkiewicz

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Dept. of Computer Science, The University of Wales, Aberystwyth, UK

    Richard Jensen

  5. Harbin Institute of Technology, PO Box 458, 150006, Harbin, China

    Qinghua Hu

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Skowron, A., Stepaniuk, J. (2010). Approximations and Classifiers. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_32

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  • DOI: https://doi.org/10.1007/978-3-642-13529-3_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13528-6

  • Online ISBN: 978-3-642-13529-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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