Attribute Selection with Fuzzy Decision Reducts

Chris Cornelis, Richard Jensen, Germán Hurtado, Dominik Slezak

Research output: Contribution to journalArticlepeer-review

221 Citations (Scopus)
366 Downloads (Pure)


Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.
Original languageEnglish
Pages (from-to)209-224
Number of pages16
JournalInformation Sciences
Issue number2
Early online date20 Sept 2009
Publication statusPublished - 15 Jan 2010


  • Rough sets
  • Fuzzy sets
  • Attribute selection
  • Data analysis
  • Decision reducts


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