Fernández J.C. , Gutiérrez P.A. , Hervás C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ
No
Proc. - Int. Conf. Hybrid Intelligent Syst., HIS
Capítulo de un Libro
Científica
01/01/2008
2-s2.0-55349131733
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and Sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a Memetic Pareto Evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop-IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one. © 2008 IEEE.
Acids; Forecasting; Intelligent control; Intelligent systems; Ketones; Organic acids; Pareto principle; Vegetation; Ascorbic acids; Classification tasks; Evolutionary Artificial Neural networks; Generalization abilities; Listeria monocytogenes; Local searches; Memetic; Monocytogenes; Neural network models; Nsga-ii; Storage temperatures; Neural networks