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Application of normalized decision measures to the new case classification

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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 optimization of rough set based classification models with respect to parameterized balance between a model’s complexity and confidence is discussed. For this purpose, the notion of a parameterized approximate inconsistent decision reduct is used. Experimental extraction of considered models from real life data is described.

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Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Dominik Ślęzak & Dominik Ślęzak

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

    Jakub Wróblewski & Jakub Wróblewski

Authors
  1. Dominik Ślęzak
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  2. Jakub Wróblewski
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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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Ślęzak, D., Wróblewski, J. (2001). Application of normalized decision measures to the new case classification. 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_69

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  • DOI: https://doi.org/10.1007/3-540-45554-X_69

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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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