| Título |
Evolutionary product-unit neural networks for classification |
| Autores |
MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Hervás-Martínez C. , Peña P.A.G. , MARTÍNEZ ESTUDILLO, ALFONSO CARLOS, Ventura-Soto S. |
| Publicación externa |
No |
| Medio |
Lect. Notes Comput. Sci. |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.31700 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750545561&doi=10.1007%2f11875581_157&partnerID=40&md5=cef024685c3af9d60ad78fde7030e593 |
| Fecha de publicacion |
01/01/2006 |
| Scopus Id |
2-s2.0-33750545561 |
| DOI |
10.1007/11875581_157 |
| Abstract |
We propose a classification method based on a special class of feedforward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-ofthe-art performance. © Springer-Verlag Berlin Heidelberg 2006. |
| Palabras clave |
Classification (of information); Error analysis; Evolutionary algorithms; Functions; Nonlinear systems; Probabilistic logics; Basis functions; Classification method; Nonlinear basis functions; Product |
| Miembros de la Universidad Loyola |
|
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