Improving the β-Precision and OWA Based Fuzzy Rough Set Models: Definitions, Properties and Robustness Analysis

  • Lynn D’eer
  • Nele Verbiest
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8536)


Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the β-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-β-PREC and a-OWA to noisy data and compare them to a general implicator-conjunctor-based fuzzy rough set model.


fuzzy sets rough sets hybridization lower and upper approximation implication conjunction beta-precision OWA robustness 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lynn D’eer
    • 1
  • Nele Verbiest
    • 1
  1. 1.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGentBelgium

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