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Type: Journal article
Title: Non-linear variable selection for artificial neural networks using partial mutual information
Author: May, R.
Maier, H.
Dandy, G.
Fernando, T.
Citation: Environmental Modelling and Software, 2008; 23(10-11):1312-1326
Publisher: Elsevier Sci Ltd
Issue Date: 2008
ISSN: 1364-8152
Statement of
Robert J. May, Holger R. Maier, Graeme C. Dandy and T.M.K. Gayani Fernando
Abstract: Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. However, the selection of an appropriate set of input variables during ANN development is important for obtaining high-quality models. This can be a difficult task when considering that many input variable selection (IVS) techniques fail to perform adequately due to an underlying assumption of linearity, or due to redundancy within the available data. This paper focuses on a recently proposed IVS algorithm, based on estimation of partial mutual information (PMI), which can overcome both of these issues and is considered highly suited to the development of ANN models. In particular, this paper addresses the computational efficiency and accuracy of the algorithm via the formulation and evaluation of alternative techniques for determining the significance of PMI values estimated during selection. Furthermore, this paper presents a rigorous assessment of the PMI-based algorithm and clearly demonstrates the superior performance of this non-linear IVS technique in comparison to linear correlation-based techniques.
Keywords: Artificial neural networks
Input variable selection
Partial mutual information
Environmental modelling
Information theory
Description: Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.
DOI: 10.1016/j.envsoft.2008.03.007
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Appears in Collections:Aurora harvest 5
Civil and Environmental Engineering publications
Environment Institute publications

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