Title Evolutionary q-Gaussian radial basis function neural networks for multiclassification
Authors FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervás-Martínez C., Gutiérrez P.A., CARBONERO RUZ, MARIANO, CARBONERO RUZ, MARIANO, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
External publication No
Means Neural Netw.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 2
JCR Impact 2.18200
SJR Impact 0.83500
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960893968&doi=10.1016%2fj.neunet.2011.03.014&partnerID=40&md5=06d893543899f10980bd629c27e28bbd
Publication date 01/01/2011
ISI 000294397200012
Scopus Id 2-s2.0-79960893968
DOI 10.1016/j.neunet.2011.03.014
Abstract This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods. © 2011 Elsevier Ltd.
Keywords Classification methods; Data sets; Design method; Experimental studies; Gaussians; Hidden layers; Hybrid algorithms; Multi-classification; Multinomial logistic regression; Multiquadratics; Probabilist
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