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Evolutionary -Gaussian radial basis function neural networks for multiclassification

✍ Scribed by Francisco Fernández-Navarro; César Hervás-Martínez; P.A. Gutiérrez; M. Carbonero-Ruz

Publisher
Elsevier Science
Year
2011
Tongue
English
Weight
564 KB
Volume
24
Category
Article
ISSN
0893-6080

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✦ Synopsis

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.

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