https://github.com/critical-infrastructure-systems-lab/iterative_input_selection
MatLab / C implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013). The underlying model (i.e. Extremely Randomized Trees) relies on C code that makes it more computationally efficient.
https://github.com/critical-infrastructure-systems-lab/iterative_input_selection
Science Score: 33.0%
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MatLab / C implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013). The underlying model (i.e. Extremely Randomized Trees) relies on C code that makes it more computationally efficient.
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README.md
IterativeInputSelection
The IterativeInputSelection toolbox is a MatLab / C implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013). The underlying Extremely Randomized Trees (Extra-Trees) models are implemented using the "rtree-c" code by P. Geurts (http://www.montefiore.ulg.ac.be/~geurts/Software.html) to improve computational efficiency.
The original version, entirely written in MATLAB, is available at https://github.com/Critical-Infrastructure-Systems-Lab/MATLABIterativeInput_Selection.
Contents:
* script_example.m: show how to use the available functions on a sample dataset (Friedmandataset.txt).
* `crossvalidationextratreeensemble.m: run a k-fold cross-validation for an ensemble of Extra-Trees.
*inputranking.m: rank the input variables.
*iterativeinputselection.m: run the IIS algorithm.
*performIIS.m: wrapper function used to launch iterative_input_selection.m
*shuffledata.m: shuffle the observations of the sample dataset.
*Rt2fit.m: compute the coefficient of determination R2.
*visualizeinputSel.m: visualize the results obtained with multiple runs of the IIS algorithm.
*Friedmandataset.txt: sample dataset, with 10 candidate inputs (first 10 columns) and 1 output (last column). The observations, arranged by rows, are 250.
*INSTALL.txt`: text file containing step-by-step instructions for modifying and compiling the C source code.
Based on work from the following papers:
- Galelli, S., Humphrey, G.B., Maier, H.R., Castelletti, A., Dandy, G.C., Gibbs, M.S. (2014) An evaluation framework for input variable selection algorithms for environmental data-driven models (2014). Environmental Modelling & Software, 62, 33-51 (Link to Paper).
- Galelli, S., and A. Castelletti (2013a), Tree-based iterative input variable selection for hydrological modeling, Water Resour. Res., 49(7), 4295-4310 (Link to Paper).
- Galelli, S., and A. Castelletti (2013b), Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling, Hydrol. Earth Syst. Sci., 17, 2669-2684 (Link to Paper).
- Geurts, P., D. Ernst, and L. Wehenkel (2006), Extremely randomized trees, Mach. Learn., 63(1), 3-42 (Link to Paper).
Acknowledgements: to Dr. Matteo Giuliani (Politecnico di Milano).
Copyright 2014 Stefano Galelli and Riccardo Taormina
This file is part of IterativeInputSelection
IterativeInputSelection is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with IterativeInputSelection. If not, see http://www.gnu.org/licenses/.
Owner
- Name: CRITICAL Infrastructure Systems Lab
- Login: Critical-Infrastructure-Systems-Lab
- Kind: organization
- Email: stefano_galelli@sutd.edu.sg
- Location: Singapore
- Website: https://people.sutd.edu.sg/~stefano_galelli/
- Twitter: GalelliStefano
- Repositories: 1
- Profile: https://github.com/Critical-Infrastructure-Systems-Lab
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