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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Keywords

feature-selection machine-learning
Last synced: 5 months ago · JSON representation

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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.

Basic Info
  • Host: GitHub
  • Owner: Critical-Infrastructure-Systems-Lab
  • Language: MATLAB
  • Default Branch: master
  • Homepage:
  • Size: 45.9 KB
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  • Forks: 6
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Topics
feature-selection machine-learning
Created over 11 years ago · Last pushed almost 3 years ago
Metadata Files
Readme

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

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