Quantum-inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling

Authors:
Stefan Schliebs;Michaël Defoin Platel;Sue Worner;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;Biomathematics and Bioinformatics at Rothamsted Research, United Kingdom;Lincoln University and Centre for Bioprotection, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a Naive Bayesian Classifier.