Structure-Based Attribute Reduction: A Rough Set Approach

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


We provide an introduction to a rough set approach to attribute reduction. Analyzed data sets consist of objects which are described by attributes and partitioned into decision classes. Rough set theory deals with uncertainty decision classes with respect to attributes by approximating them to precise sets. The aim of attribute reduction is to remove redundant attributes as well as find important ones for classification. Several types of attribute reduction have been proposed especially according to preserving structures of approximated decision classes. We introduce definitions and theoretical results about structures-based attribute reduction.


Rough set model Reduct Boolean function Structure-based reduct 


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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka UniversitySuita, OsakaJapan
  2. 2.Graduate School of Engineering SciencesOsaka UniversityToyonaka, OsakaJapan

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