aiqbench analysis
This research extends evolutionary feature selection techniques to the biologically realistic JASTAP spiking neural network model, demonstrating improved robustness against noise on standard datasets. By enabling smaller network architectures for classification tasks, the work advances the intersection of machine learning algorithms and neuro-inspired computing models.
Evolutionary feature selection for spiking neural network pattern classifiers
arXiv:2604.26654v1 Announce Type: cross 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.
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.
Published at Portuguese Conference on Artificial Intelligence (EPIA 2005), eds. Bento et al., IEEE, pp. 24-32
Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
arXiv:2604.26654 [cs.NE]
(or arXiv:2604.26654v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2604.26654 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 200 words
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