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Variable Precision Approximations of Rough Sets

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Granular, Fuzzy, and Soft Computing

Part of the book series: Encyclopedia of Complexity and Systems Science Series ((ECSSS))

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  • Originally published in
  • R. A. Meyers (ed.), Encyclopedia of Complexity and Systems Science, © Springer Science+Business Media LLC 2021

Abstract

This entry reviews the key elements of the variable precision rough set model. The relevant aspects of the original Pawlak’s rough set model and of the Bayesian rough set model are also discussed. The notion of probabilistic decision tables learned from data is presented. Techniques for the detection and analysis of data dependencies appearing in probabilistic decision tables are investigated. Methods for probabilistic decision table reduction are shown to produce minimal representations of probabilistic data dependencies. The presented methodology is applicable, among others, to machine learning, data mining, sensor-based control systems, and data analysis in general.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, Canada

    Wojciech Ziarko

Authors
  1. Wojciech Ziarko
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Correspondence to Wojciech Ziarko .

Editor information

Editors and Affiliations

  1. Institute of Data Science, GrC Society, San Jose, CA, USA

    Tsau-Young Lin

  2. Inst of Information Science, Academia Sinica, Taipei, Taiwan

    Churn-Jung Liau

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Ziarko, W. (2023). Variable Precision Approximations of Rough Sets. In: Lin, TY., Liau, CJ., Kacprzyk, J. (eds) Granular, Fuzzy, and Soft Computing. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2628-3_719

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