KEDRI - the Knowledge Engineering and Discovery Research Institute

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https://hdl.handle.net/10292/552
KEDRI - the Knowledge Engineering and Discovery Research Institute of Auckland University of Technology was established in June 2002 and since then has been developing novel information processing methods, technologies and their applications to enhance discoveries across different areas of science and engineering. The methods are mainly based on principles from Nature, such as brain information processing, evolution, genetics, quantum physics.

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Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Author "Defoin-Platel, M"

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    Integrated feature and parameter optimization for an evolving spiking neural network
    (Springer, 2009) Schliebs, S; Defoin-Platel, M; Kasabov, N; Koppen, M; Kasabov, N; Coghill, G
    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.
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    Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling
    (IEEE, 2009-06-14) Schliebs, S; Defoin-Platel, M; Worner, S; Kasabov, N
    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.
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