Elements of information theory
Elements of information theory
Diagnosing chaos by a fuzzy classifier
Fuzzy Sets and Systems
An estimator of the mutual information based on a criterion for independence
Computational Statistics & Data Analysis
Environmental Modelling & Software
Environmental Modelling & Software
Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Selection and validation of parameters in multiple linear and principal component regressions
Environmental Modelling & Software
A deterministic air quality forecasting system for Torino urban area, Italy
Environmental Modelling & Software
Environmental Modelling & Software
Effective feature selection scheme using mutual information
Neurocomputing
Mathematical and Computer Modelling: An International Journal
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Environmental Modelling & Software
Environmental Modelling & Software
Predicting the potential habitat of oaks with data mining models and the R system
Environmental Modelling & Software
Environmental Modelling & Software
A feature selection method for air quality forecasting
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A generic framework for regression regionalization in ungauged catchments
Environmental Modelling & Software
Data-driven dynamic emulation modelling for the optimal management of environmental systems
Environmental Modelling & Software
Environmental Modelling & Software
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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.