Attribute Selection with Fuzzy Decision Reducts

Chris Cornelis, Richard Jensen, Germán Hurtado, Dominik Slezak

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

289 Wedi eu Llwytho i Lawr (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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)209-224
Nifer y tudalennau16
CyfnodolynInformation Sciences
Rhif cyhoeddi2
Dyddiad ar-lein cynnar20 Medi 2009
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 15 Ion 2010

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