Integrated feature and parameter optimization for an evolving spiking neural network

Authors:
Stefan Schliebs;Michaël Defoin-Platel;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;Department of Computer Science, University of Auckland;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following the wrapper approach, the method is used to identify relevant feature subsets and simultaneously evolve an optimal ESNN parameter setting. Applied to carefully designed benchmark data, containing irrelevant and redundant features of varying information quality, the ESNN-based feature selection procedure lead to excellent classification results and an accurate detection of relevant information in the dataset. Redundant and irrelevant features were rejected successively and in the order of the degree of information they contained.