Title :
Study of the Robustness of a Meta-Algorithm for the Estimation of Parameters in Artificial Neural Networks Design
Author :
Parras-Gutierrez, Elisabet ; Jesus, M. Jose del ; Rivas, Victor M. ; Merelo, Juan J.
Author_Institution :
Dept. of Comput. Sci., Univ. of Jaen, Jaen
Abstract :
Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm developed to automatically establish the parameters needed to design RBFNs. Results show that this new method can be effectively used, not only to obtain good models, but also to find a stable set of parameters, available to be used on many different problems.
Keywords :
data mining; evolutionary computation; learning (artificial intelligence); pattern classification; radial basis function networks; artificial neural network design; classification problem; data mining algorithm; evolutionary algorithm; machine learning; meta-algorithm robustness; parameter estimation; radial basis function networks; Algorithm design and analysis; Artificial neural networks; Data mining; Evolutionary computation; Genetic mutations; Hybrid intelligent systems; Neurons; Parameter estimation; Radial basis function networks; Robustness; Meta-evolution; Radial basis neural network; parameter estimation;
Conference_Titel :
Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3326-1
Electronic_ISBN :
978-0-7695-3326-1
DOI :
10.1109/HIS.2008.66
