The Iterative_Input_Selection 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/MATLAB_Iterative_Input_Selection.
Contents:
script_example.m: show how to use the available functions on a sample dataset (Friedman_dataset.txt).crossvalidation_extra_tree_ensemble.m: run a k-fold cross-validation for an ensemble of Extra-Trees.input_ranking.m: rank the input variables.iterative_input_selection.m: run the IIS algorithm.perform_IIS.m: wrapper function used to launch iterative_input_selection.mshuffle_data.m: shuffle the observations of the sample dataset.Rt2_fit.m: compute the coefficient of determination R2.visualize_inputSel.m: visualize the results obtained with multiple runs of the IIS algorithm.Friedman_dataset.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 Iterative_Input_Selection
Iterative_Input_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.
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