Ordered Weighted Average Based Fuzzy Rough Sets

  • Chris Cornelis
  • Nele Verbiest
  • Richard Jensen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)


Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied to data analysis problems, these approximations are sensitive to noisy and/or outlying samples. In this paper, we advocate a mitigated approach, in which membership to the lower and upper approximation is determined by means of an aggregation process using ordered weighted average operators. In comparison to the previously introduced vaguely quantified rough set model, which is based on a similar rationale, our proposal has the advantage that the approximations are monotonous w.r.t. the used fuzzy indiscernibility relation. Initial experiments involving a feature selection application confirm the potential of the OWA-based model.


fuzzy rough sets vaguely quantified rough sets ordered weighted average aggregation operators noise tolerance data analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chris Cornelis
    • 1
  • Nele Verbiest
    • 1
  • Richard Jensen
    • 2
  1. 1.Department of Applied Mathematics and Computer ScienceGhent UniversityGentBelgium
  2. 2.Department of Computer ScienceAberystwyth UniversityWalesUK

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