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Rough Numbers and Rough Regression

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Book cover Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6743))

Abstract

In this article a new model of regression is defined. On the basis of the rough sets theory a notion of rough number is defined. Typical real numbers calculations do not keep the additional information like the uncertainty or the error of input data. Rough numbers remove this limitation. It causes that rough numbers seem to be interested as the basis of the new way of regression: rough regression.

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References

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Author information

Authors and Affiliations

  1. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Marcin Michalak

Authors
  1. Marcin Michalak
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Editor information

Editors and Affiliations

  1. State University Higher School of Economics, Pokrovskiy Bd. 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

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

    Dominik Ślęzak

  3. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Daryl H. Hepting

  4. Birbeck University of London, Malet Street, WC1E 7HX, London, UK

    Boris G. Mirkin

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© 2011 Springer-Verlag Berlin Heidelberg

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Michalak, M. (2011). Rough Numbers and Rough Regression. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-21881-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21880-4

  • Online ISBN: 978-3-642-21881-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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