The MATLAB_IterativeInputSelection toolbox is a MatLab implementation of the Iterative Input Selection (IIS) algorithm proposed by Galelli and Castelletti (2013).
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"Update August, 2015: the toolbox can now implement the IIS algorithm for classification problem. The MATLAB_ExtraTrees toolbox has been accordingly updated as well".

The MATLAB_IterativeInputSelection 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 MATLAB_IterativeInputSelection toolbox requires the MATLAB_ExtraTrees toolbox, which can be found at


Contents of MATLAB_IterativeInputSelection :

  • 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.
  • visualize_inputSel.m: visualize the results obtained with multiple runs of the IIS algorithm.
  • shuffle_data.m: shuffle the observations of the sample dataset.
  • Rt2_fit.m: compute the coefficient of determination R2.
  • Friedman_dataset.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., 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) and Riccardo Taormina (Hong Kong Polytechnic University).

Copyright 2015 Ahmad Alsahaf Research fellow, Politecnico di Milano

Copyright 2014 Stefano Galelli, Assistant Professor, Singapore University of Technology and Design, E-mail: (Link to Homepage).

This file is part of MATLAB_IterativeInputSelection.

MATLAB_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 MATLAB_IterativeInputSelection. If not, see