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Soft Computing
Erschienen in: Soft Computing 2/2013

01.02.2013 | Focus

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

verfasst von: J. Derrac, N. Verbiest, S. García, C. Cornelis, F. Herrera

Erschienen in: Soft Computing | Ausgabe 2/2013

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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.
Vorheriger Artikel Evolutionary optimization of multi-parametric kernel -SVMr for forecasting problems
Nächster Artikel A differential inclusion approach for modeling and analysis of dynamical systems under uncertainty

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Fußnoten
4
The experiments have been carried out on a machine with a Dual Core 3,20 GHz processor and 2GB of RAM, running under the Fedora 4 operating System.
 
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Metadaten
Titel
On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection
verfasst von
J. Derrac
N. Verbiest
S. García
C. Cornelis
F. Herrera
Publikationsdatum
01.02.2013
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 2/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0888-3

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