| Título |
Design of artificial neural networks using a memetic pareto evolutionary algorithm using as objectives entropy versus variation coefficient |
| Autores |
Fernández J.C. , Hervás C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Cruz M. |
| Publicación externa |
No |
| Medio |
Isda 2009 - 9th International Conference On Intelligent Systems Design And Applications |
| Alcance |
Capítulo de un Libro |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949498372&doi=10.1109%2fISDA.2009.153&partnerID=40&md5=572f6042c1391ba23894b6dc36594def |
| Fecha de publicacion |
01/01/2009 |
| Scopus Id |
2-s2.0-77949498372 |
| DOI |
10.1109/ISDA.2009.153 |
| Abstract |
This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a Memetic Pareto Evolutionary approach based on the 2SGA2 algorithm (MPE2SGA2), where we defined two objectives for determining the goodness of a classifier: the cross-entropy error function and the variation coefficient of its sensitivities, because both measures are continuous functions, making the convergence more robust. Once the Pareto front is built, we use an automatic selection methodology of individuals: the best model in accuracy (upper extreme in the Pareto front). This methodology is applied to solve six benchmark classification problems, obtaining promising results and achieving a high classification rate in the generalization set with an acceptable level of accuracy for each class. © 2009 IEEE. |
| Palabras clave |
Artificial Neural Network; Automatic selection; Benchmark classification; Best model; Classification rates; Continuous functions; Cross entropy; Error function; Evolutionary approach; Machine learning problem; Memetic; Multi-classification; Multi-layer perceptron neural networks; Pareto front; Variation coefficient; Classifiers; Convergence of numerical methods; Design; Evolutionary algorithms; Intelligent systems; Learning algorithms; Multilayer neural networks; Sensitivity analysis; Pareto principle |
| Miembros de la Universidad Loyola |
|
Gestionar cookies