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dc.creatorFernández Navarro, Franciscoes
dc.creatorHervás Martínez, Césares
dc.creatorRuiz, Robertoes
dc.creatorRiquelme Santos, José Cristóbales
dc.identifier.citationFernández Navarro, F., Hervás Martínez, C., Ruíz, R. y Riquelme Santos, J.C. (2012). Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection. Applied Soft Computing, 12 (6), 1787-1800.
dc.description.abstractRadial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly repro-duce different RBFs by changing a real parameter . In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, , to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classi-fication problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in
dc.description.sponsorshipMICYT TIN 2008-06681- C06-03es
dc.description.sponsorshipJunta de Andalucia P08-TIC- 3745es
dc.relation.ispartofApplied Soft Computing, 12 (6), 1787-1800.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.subjectGeneralized Radial Basis Functiones
dc.subjectGeneralized Gaussian Distributiones
dc.subjectevolutionary algorithmses
dc.subjectGene classificationes
dc.subjectFeature selectiones
dc.titleEvolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selectiones
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN 2008-06681- C06-03es
dc.relation.projectIDP08-TIC- 3745es
dc.journaltitleApplied Soft Computinges
dc.contributor.funderMinisterio de Ciencia y Tecnología (MCYT). España
dc.contributor.funderJunta de Andalucía

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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