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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Maass, W.: Computing with spiking neurons (1999)
Verstraeten, D., Schrauwen, B., Stroobandt, D.: Isolated word recognition using a liquid state machine. In: ESANN, pp. 435–440 (2005)
Bohte, S.M., Kok, J.N., Poutré, J.A.L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1-4), 17–37 (2002)
Knoblauch, A.: Neural associative memory for brain modeling and information retrieval. Inf. Process. Lett. 95(6), 537–544 (2005)
Wysoski, S.G., Benuskova, L., Kasabov, N.: On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 61–70. Springer, Heidelberg (2006)
Wysoski, S., Benuskova, L., Kasabov, N.: Brain-like evolving spiking neural networks for multimodal information processing. In: ICONIP 2007. LNCS. Springer, Heidelberg (2007)
Soltic, S., Wysoski, S., Kasabov, N.: Evolving spiking neural networks for taste recognition. In: IEEE World Congress on Computational Intelligence (WCCI), Hong Kong (2008)
VanRullen, R., Thorpe, S.J.: Is it a bird? is it a plan? ultra-rapid visual categorisation of natural and artificial objects. Perception 30, 655–668 (2001)
Thorpe, S.J.: How can the human visual system process a natural scene in under 150ms? experiments and neural network models. In: ESANN (1997)
Wysoski, S.G.: Evolving Spiking Neural Networks for Adaptive Audiovisual Pattern Recognition. PhD thesis, Auckland University of Technology (August 2008), http://hdl.handle.net/10292/390
Defoin-Platel, M., Schliebs, S., Kasabov, N.: A versatile quantum-inspired evolutionary algorithm. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 423–430 (2007)
Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired evolutionary algorithm: A multimodel eda. IEEE Transactions on Evolutionary Computation (in print, 2009)
Valko, M., Marques, N.C., Castelani, M.: Evolutionary feature selection for spiking neural network pattern classifiers. In: Bento, et al. (eds.) Proceedings of 2005 Portuguese Conference on Artificial Intelligence, pp. 24–32. IEEE, Los Alamitos (2005)
Lang, K.J., Witbrock, M.J.: Learning to tell two spirals apart. In: Proceedings of the 1988 Connectionist Models Summer School San Mateo, pp. 52–59. Morgan Kauffmann, San Francisco (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schliebs, S., Defoin-Platel, M., Kasabov, N. (2009). Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_149
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_149
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)