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An Investigation of β-Reduct Selection within the Variable Precision Rough Sets Model

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Rough Sets and Current Trends in Computing (RSCTC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2005))

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Abstract

The Variable Precision Rough Sets Model (VPRS) is an extension of the original Rough Set Theory. To employ VPRS analysis the decision maker (DM) needs to define satisfactory levels of quality of classification and β (confidence) value. This paper considers VPRS analysis when the DM only defines a satisfactory level of quality of classification. Two criteria for selecting a β-reduct under this condition are discussed. They include the use of permissible β intervals associated with each β-reduct. An example study is given illustrating these criteria. The study is based on US state level data concerning motor vehicle traffic fatalities.

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Author information

Authors and Affiliations

  1. Cardiff Business School, Cardiff University, Colum Drive, Cardiff, CF10 3EU, Wales, UK

    Malcolm Beynon

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  1. Malcolm Beynon
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina Regina, S4S 0A2, Saskatchewan, Canada

    Wojciech Ziarko  & Yiyu Yao  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Beynon, M. (2001). An Investigation of β-Reduct Selection within the Variable Precision Rough Sets Model. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_13

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  • DOI: https://doi.org/10.1007/3-540-45554-X_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43074-2

  • Online ISBN: 978-3-540-45554-7

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