Integrated feature and parameter optimization for an evolving spiking neural network

Date
2009
Authors
Schliebs, S
Defoin-Platel, M
Kasabov, N
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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.

Description
Keywords
Neurons
Source
15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I, Lecture Notes in Computer Science Volume 5506, 2009, pages 1229 - 1236
Publisher's version
Rights statement
© Springer-Verlag Berlin Heidelberg 2009. An author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository.