Title Selecting the Best Artificial Neural Network Model from a Multi-Objective Differential Evolution Pareto Front
Authors Cruz-Ramirez, M. , Fernandez, J. C. , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, SÁNCHEZ MONEDERO, JAVIER, Hervas-Martinez, C.
External publication Si
Means Ieee Symposium On Differential Evolution
Scope Proceedings Paper
Nature Científica
Publication date 01/01/2011
ISI 000310380700013
Abstract The objective of this work is to select artificial neural network models (ANN) automatically with sigmoid basis units for multiclassification tasks. These models are designed using a Memetic Pareto Differential Evolution Neural Network algorithm (MPDENN) based on the Pareto dominance concept. We propose different methodologies to obtain the best model from the Pareto front obtained with the MPDENN algorithm. These methodologies are based on choosing the best models for training in both objectives, the Correct Classification Rate and Minimum Sensitivity, and the two models closest to the centroids of two clusters formed with the models of the first and second Pareto fronts. These methodologies are compared with three standard ensembles methodologies with very competitive results.
Universidad Loyola members

Change your preferences Manage cookies