https://github.com/critical-infrastructure-systems-lab/matlab_iterative_input_selection
MatLab implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013).
https://github.com/critical-infrastructure-systems-lab/matlab_iterative_input_selection
Science Score: 33.0%
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MatLab implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013).
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README.md
MATLABIterativeInput_Selection
"Update August, 2015: the toolbox can now implement the IIS algorithm for classification problems. The MATLAB_ExtraTrees toolbox has been accordingly updated as well".
The MATLABIterativeInput_Selection toolbox is a MatLab implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013a). The underlying regression method adopted by the IIS algorithm is an ensemble of Extra-Trees (Geurts et al., 2006; Galelli and Castelletti, 2013b). The user is referred to the original publication for details regarding the IIS algorithm.
The MATLABIterativeInputSelection toolbox requires the MATLABExtraTrees toolbox, which can be found at https://github.com/rtaormina/MATLAB_ExtraTrees.
!!!!! ATTENTION !!!!! A FASTER VERSION OF THE IIS ALGORITHM THAT EMPLOYS Extra-Trees WRITTEN IN C IS NOW AVAILABLE AT https://github.com/Critical-Infrastructure-Systems-Lab/IterativeInputSelection.
Contents of MATLABIterativeInputSelection :
* `scriptexample.m: show how to use the available functions on a sample dataset (Friedman_dataset.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.
*visualizeinputSel.m: visualize the results obtained with multiple runs of the IIS algorithm.
*shuffledata.m: shuffle the observations of the sample dataset.
*Rt2fit.m: compute the coefficient of determination R2.
*Friedmandataset.txt`: sample dataset, with 10 candidate inputs (first 10 columns) and 1 output (last column). The observations, arranged by rows, are 250.
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), Riccardo Taormina (TU Delft), and Ahmad Alsahaf (Politecnico di Milano).
Copyright 2014 Stefano Galelli.
This file is part of MATLABIterativeInput_Selection.
MATLABIterativeInput_Selection 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 MATLABIterativeInput_Selection. 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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