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Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers

Abstract : This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension allows the use of smaller neural networks that can handle noisier data without any degradation in classification accuracy.
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Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Tuesday, November 22, 2011 - 10:09:43 AM
Last modification on : Wednesday, November 10, 2021 - 5:18:05 PM
Long-term archiving on: : Friday, November 16, 2012 - 11:40:38 AM


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Michal Valko, Nuno Cavalheiro, Marco Castelani. Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers. Proceedings of 2005 Portuguese Conference on Artificial Intelligence, Dec 2005, Covilha, Portugal. pp.181-187, ⟨10.1109/EPIA.2005.341291⟩. ⟨hal-00643498⟩



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