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

(2013) SOFT COMPUTING. 17(2). p.223-238
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Abstract
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.
Keywords
Data reduction, Feature selection, Fuzzy rough sets, Evolutionary algorithms, Nearest neighbor, NEAREST-NEIGHBOR CLASSIFICATION, FEATURE SUBSET-SELECTION, INSTANCE SELECTION, GENETIC-ALGORITHM, LEARNING ALGORITHMS, REDUCTION, RULE, INTERPRETABILITY, NETWORKS, DESIGN, :Prototype selection

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Citation

Please use this url to cite or link to this publication:

MLA
Derrac, Joaquin, Nele Verbiest, Salvador García, et al. “On the Use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection.” SOFT COMPUTING 17.2 (2013): 223–238. Print.
APA
Derrac, Joaquin, Verbiest, N., García, S., Cornelis, C., & Herrera, F. (2013). On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection. SOFT COMPUTING, 17(2), 223–238.
Chicago author-date
Derrac, Joaquin, Nele Verbiest, Salvador García, Chris Cornelis, and Francisco Herrera. 2013. “On the Use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection.” Soft Computing 17 (2): 223–238.
Chicago author-date (all authors)
Derrac, Joaquin, Nele Verbiest, Salvador García, Chris Cornelis, and Francisco Herrera. 2013. “On the Use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection.” Soft Computing 17 (2): 223–238.
Vancouver
1.
Derrac J, Verbiest N, García S, Cornelis C, Herrera F. On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection. SOFT COMPUTING. 2013;17(2):223–38.
IEEE
[1]
J. Derrac, N. Verbiest, S. García, C. Cornelis, and F. Herrera, “On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection,” SOFT COMPUTING, vol. 17, no. 2, pp. 223–238, 2013.
@article{4123277,
  abstract     = {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.},
  author       = {Derrac, Joaquin and Verbiest, Nele and García, Salvador and Cornelis, Chris and Herrera, Francisco},
  issn         = {1432-7643},
  journal      = {SOFT COMPUTING},
  keywords     = {Data reduction,Feature selection,Fuzzy rough sets,Evolutionary algorithms,Nearest neighbor,NEAREST-NEIGHBOR CLASSIFICATION,FEATURE SUBSET-SELECTION,INSTANCE SELECTION,GENETIC-ALGORITHM,LEARNING ALGORITHMS,REDUCTION,RULE,INTERPRETABILITY,NETWORKS,DESIGN,:Prototype selection},
  language     = {eng},
  number       = {2},
  pages        = {223--238},
  title        = {On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection},
  url          = {http://dx.doi.org/10.1007/s00500-012-0888-3},
  volume       = {17},
  year         = {2013},
}

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