Soft Computing

, Volume 17, Issue 2, pp 223–238 | Cite as

On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection

  • J. Derrac
  • N. Verbiest
  • S. García
  • C. Cornelis
  • F. Herrera


The k-nearest neighbors classifier is a widely used classification method that has proven to be very effective in supervised learning tasks. In this paper, a fuzzy rough set method for prototype selection, focused on optimizing the behavior of this classifier, is presented. The hybridization with an evolutionary feature selection method is considered to further improve its performance, obtaining a competent data reduction algorithm for the 1-nearest neighbors classifier. This hybridization is performed in the training phase, by using the solution of each preprocessing technique as the starting condition of the other one, within a cycle. The results of the experimental study, which have been contrasted through nonparametric statistical tests, show that the new hybrid approach obtains very promising results with respect to classification accuracy and reduction of the size of the training set.


Prototype selection Feature selection Data reduction Fuzzy rough sets Evolutionary algorithms Nearest neighbor 


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • J. Derrac
    • 1
  • N. Verbiest
    • 2
  • S. García
    • 3
  • C. Cornelis
    • 1
    • 2
  • F. Herrera
    • 1
  1. 1.Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology)University of GranadaGranadaSpain
  2. 2.Department of Applied Mathematics and Computer ScienceGhent UniversityGhentBelgium
  3. 3.Department of Computer ScienceUniversity of JaénJaénSpain

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