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Dependency Analysis and Attribute Reduction in the Probabilistic Approach to Rough Sets

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Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

Abstract

Two probabilistic approaches to rough sets are discussed in this chapter: the variable precision rough set model and the Bayesian rough set model, as they apply to data dependencies detection, analysis and their representation. The focus is on the analysis of data co-occurrence-based dependencies appearing in classification tables and probabilistic decision tables acquired from data. In particular, the notion of attribute reduct, in the framework of probabilistic approach, is of interest in the chapter. The reduct allows for information-preserving elimination of redundant attributes from classification tables and probabilistic decision tables. The chapter includes two efficient reduct computation algorithms.

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Acknowledgments

Thanks are due to anonymous referees for their detailed and inspiring comments. The research reported in the chapter was supported by research grants from Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

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

    Wojciech Ziarko

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  1. Wojciech Ziarko
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Correspondence to Wojciech Ziarko .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Ziarko, W. (2015). Dependency Analysis and Attribute Reduction in the Probabilistic Approach to Rough Sets. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_6

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  • DOI: https://doi.org/10.1007/978-3-662-45620-0_6

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  • Online ISBN: 978-3-662-45620-0

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