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On Model Evaluation, Indexes of Importance, and Interaction Values in Rough Set Analysis

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Rough-Neural Computing

Part of the book series: Cognitive Technologies ((COGTECH))

Summary

As with most data models, “computing with words” uses a mix of methods to achieve its aims, including several measurement indexes. In this chapter, we discuss some proposals for such indexes in the context of rough set analysis, and we present some new ones.

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Author information

Authors and Affiliations

  1. Institut für Evaluation und Marktanalysen, Brinkstr. 19, Jeggen, Germany

    Günther Gediga

  2. Department of Computer Science, Brock University, St. Catherines, Ontario L2S 3AI, Canada

    lvo Düntsch

Authors
  1. Günther Gediga
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  2. lvo Düntsch
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Machine Intelligence Unit, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal

  2. Department of Mathematics and Computer Science, University of Warmia and Mazury, Zolnierska 14, Olsztyn, Poland

    Lech Polkowski

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

    Andrzej Skowron

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© 2004 Springer-Verlag Berlin Heidelberg

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Gediga, G., Düntsch, l. (2004). On Model Evaluation, Indexes of Importance, and Interaction Values in Rough Set Analysis. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-18859-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62328-8

  • Online ISBN: 978-3-642-18859-6

  • eBook Packages: Springer Book Archive

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