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Approximation of Loss and Risk in Selected Granular Systems

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

Abstract

We discuss the notion of risk in generally understood classification support systems. We consider the situation when granularity is involved in information system we work with. We propose a method for approximating the loss function and introduce a technique for assessing the empirical risk from experimental data. We discuss the general methodology and possible directions of development in the area of constructing compound classification schemes.

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Author information

Authors and Affiliations

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

    Marcin Szczuka

Authors
  1. Marcin Szczuka
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Szczuka, M. (2009). Approximation of Loss and Risk in Selected Granular Systems. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-10646-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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