Non-linear variable selection for artificial neural networks using partial mutual information

By Robert J. May, Holger R. Maier, Graeme C. Dandy, T. Fernando. Environmental Modelling & Software, 2008

312 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/5029d2f85303ea32d9636c7393f714d7bcf33159

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Citations in the corpus

Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “, the EDI) could potentially be identified by nonlinear input variable selection (May et al. 2008; Quilty et al. 2016).”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “This avoids involving heuristic techniques to account for redundancy and complementarity that is accounted for with nonlinear regression models in recent PMI approaches [Bowden et al., 2005a; May et al., 2008a; Chen et al., 2014a].”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, unknown): “…correlation analysis, 370 Sudheer et al., 2002), AMI (average mutual information, Fraser and Swinney, 1986), PMI 371 (partial mutual information, May et al., 2008), FNN (false nearest neighbors, Kennel et al., 372 1992), CI (correlation integral, Theiler, 1986), SLR, and MOGA (ANN based…”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “Alternatively, the use of techniques for the selection of inputs to data-driven models (Bowden et al., 2005a, 2005b; Fernando et al., 2009; Galelli and Castelletti, 2013; Galelli et al., 2014; May et al., 2008a; May et al., 2008b) might be worthwhile.”
Estimating information entropy for hydrological data: One‐dimensional case (2014, background): “…2009b], input variable selection for seasonal and interannual rainfall probabilistic forecasts [Sharma et al., 2000; Sharma, 2000a, 2000b; Fernando et al., 2009; May et al., 2008], and quantification of the skill of ensemble forecasts [Weijs et al., 2010a, 2010b; Weijs and van de Giesen, 2011].”
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions (2010, methodology): “Classic examples of this are the partial correlation and partial mutual information input variable selection algorithms (see May et al., 2008a, b), which combine a stepwise partial modelling approach that caters for input independence (redundancy) with an analytical measure of statistical…”