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

  • Wojciech Ziarko
Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Variable precision rough set model Bayesian rough set model Dependency analysis Reduct 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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