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Non-linear variable selection for artificial neural networks using partial mutual information
Authors:Robert J May  Holger R Maier  Graeme C Dandy  TMK Gayani Fernando
Affiliation:aResearch and Development, United Water, GPO Box 1875, Adelaide, SA 5001, Australia;bSchool of Civil, Environmental, and Mining Engineering, University of Adelaide, North Terrace, Adelaide, SA 5005, Australia
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
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