Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems

This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. The enhanced methodology has been tried out with four filters using 18 data sets that report test error rates about 20 % or above with reference classifiers such as C4.5 or 1-NN. The proposal has also been evaluated in a liver-transplantation real-world problem with serious troubles in the data distribution and classifiers get low performance. The study includes an overall empirical comparison between the models obtained with and without feature selection using different kind of neural networks, like RBF, MLP and other state-of-the-art classifiers. Statistical tests show that our proposal significantly improves the test accuracy of the previous models. The reduction percentage in the number of inputs is, on average, above 55 %, thus a greater efficiency is achieved.MICYT TIN2007-68084- C02-02MICYT TIN2008-06681-C06-03MICYT TIN2011-28956-C0

New training approaches for classification based on evolutionary neural networks. Application to product and sigmoidal units

This paper sums up the main contributions of the PhD Dissertation with an homonymous name to the current article. Specifically, three contributions to train feed-forward neural network models based on evolutionary computation for a classification task are described. The new methodologies have been evaluated in three-layered neural models, including one input, one hidden and one output layer. Particularly, two kind of neurons such as product and sigmoidal units have been considered in an independent fashion for the hidden layer. Experiments have been carried out in a good number of problems, including three complex real-world problems, and the overall assessment of the new algorithms is very outstanding. Statistical tests shed light on that significant improvements were achieved. The applicability of the proposals is wide in the sense that can be extended to any kind of hidden neuron, either to other kind of problems like regression or even optimization with special emphasis in the two first approaches

2nd International Conference on Numerical and Symbolic Computation

The Organizing Committee of SYMCOMP2015 – 2nd International Conference on Numerical and Symbolic Computation: Developments and Applications welcomes all the participants and acknowledge the contribution of the authors to the success of this event. This Second International Conference on Numerical and Symbolic Computation, is promoted by APMTAC - Associação Portuguesa de Mecânica Teórica, Aplicada e Computacional and it was organized in the context of IDMEC/IST - Instituto de Engenharia Mecânica. With this ECCOMAS Thematic Conference it is intended to bring together academic and scientific communities that are involved with Numerical and Symbolic Computation in the most various scientific area