Abstract
Recently, a novelty multinomial logistic regression method where the initial covariate space is increased by adding the nonlinear transformations of the input variables given by Gaussian Radial Basis Functions (RBFs) obtained by an Evolutionary Algorithm was proposed. However, there still exist some problems with the standard Gaussian RBF, for example, the approximation of constant valued functions or the approximation of high dimensionality associated to some real problems. In order to face of these problems, we propose the use of the Generalized Gaussian RBF (GRBF) instead of the standard Gaussian RBF. Our approach has been validated with a real problem of disability classification, to evaluate its effectiveness. Experimental results show that this approach is able to achieve good generalization performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Castano, A., Hervás-Martínez, C., Gutiérrez, P.A., Fernández-Navarro, F., Garcia, M.M.: Classification by evolutionary generalized radial basis functions. In: ISDA, pp. 203–208. IEEE Computer Society, Los Alamitos (2009)
Corchado, E., Arroyo, Á., Tricio, V.: Soft computing models to identify typical meteorological days. Logic Journal of IGPL (2010) (in press)
Gutiérrez, P.A., Hervás-Martínez, C., Lozano, M.: Designing multilayer perceptrons using a guided saw-tooth evolutionary programming algorithm. Soft Computing 14(4), 599–613 (2010)
Gutiérrez, P.A., Hervás-Matínez, C., Martínez-Estudillo, F.J.: Logistic regression by means of evolutionary radial basis function neural networks. IEEE Transactions on Neural Networks (in press)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Machine Learning 59(1-2), 161–205 (2005)
Martínez-Estudillo, F.J., Hervás-Martínez, C., Gutiérrez, P.A., Martínez-Estudillo, A.C.: Evolutionary product-unit neural networks classifiers. Neurocomputing 72(1-2), 548–561 (2008)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Data Management Systems. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Méndez, A.C., Fernández-Navarro, F., Gutiérrez, P.A., Baena-García, M., Hervás-Martínez, C. (2011). Combining Evolutionary Generalized Radial Basis Function and Logistic Regression Methods for Classification. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-19644-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19643-0
Online ISBN: 978-3-642-19644-7
eBook Packages: EngineeringEngineering (R0)