| 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 |
| External publication |
No |
| Means |
Neural Networks |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
2 |
| JCR Impact |
2.182 |
| SJR Impact |
0.835 |
| 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; Probabilistic classifiers; Radial basis function neural networks; Radial basis functions; Sparse classifiers; UCI repository; Evolutionary algorithms; Gaussian distribution; Image segmentation; Neural networks; Regression analysis; Support vector machines; Radial basis function networks; article; artificial neural network; classification algorithm; classifier; controlled study; experimental study; intermethod comparison; logistic regression analysis; priority journal; q Gaussian radial basis function neural network; radial based function; support vector machine; Algorithms; Artificial Intelligence; Neural Networks (Computer); Normal Distribution |
| Universidad Loyola members |
|
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