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View Code? Open in Web Editor NEWMatLab implementation of W-QEISS, F-QEISS and W-MOSS: three algorithms for the selection of (quasi) equally informative subsets
MatLab implementation of W-QEISS, F-QEISS and W-MOSS: three algorithms for the selection of (quasi) equally informative subsets
This toolbox implements the WQEISS/WMOSS/FQEISS feature selection techniques for classification problems presented in Karakaya et al. (2015) and Taormina et al. (2016). This toolbox depends on the following contributions, which have to be donwloaded and appropriately referenced in the code. 1) Hanchuan Peng's Mutual Information computation toolbox http://www.mathworks.com/matlabcentral/fileexchange/14888-mutual-information-computation 2)Yi Cao's Pareto-front toolbox http://www.mathworks.com/matlabcentral/fileexchange/17251-pareto-front 3) Song Lin's NGPM toolbox for NSGA-II (Deb et al., 2002) http://in.mathworks.com/matlabcentral/fileexchange/31166-ngpm-a-nsga-ii-program-in-matlab-v1-4 The scripts also support the Borg multi-objective algorithm, which was originally used for the experiments reported in Karakaya et al. (2015). Interested users who want to employ Borg instead of NSGA-II are referred to http://borgmoea.org for the MATLAB files required by this package. The NSGA-II version of the algorithms is illustrated in "script_example_NSGAII.m" for the "Heart" dataset of the UCI Repository (Lichman, 2013). Users may refer to "script_example_BORG.m" for the equivalent version in Borg. NOTE: Contrary to the experiments reported in Karakaya et al. (2015), this illustrative implementation features only one run for each algorithm on the chosen dataset. We suggest the user to run each algorithm several times, possibly using different randomizations of the employed dataset, in order to maximize the number of solutions returned by the methods and better assess the accuracy of the trained models. An overall Pareto-front should then be constructed from all the solutions returned by the multiple runs, making sure that the same value of accuracy is assigned to equal solutions (equal subsets) returned on different runs. This could be done by averaging the accuracies across the runs. For a fair comparison of the results of the three algorithms, it is also important that the same (average) accuracy is assigned for the same solutions returned by the different techniques. *** UPDATE 08/2016: the W-QEISS algorithm for regression problems is described in Taormina, R., Galelli, S., Karakaya, G., Ahipasaoglu, S.D., 2016. An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models. J. Hydro. doi:10.1016/j.jhydrol.2016.07.045. See script_example_BORG__REGRESSION and script_example_NSGA__REGRESSION *** References: Karakaya, G., Galelli, S., Ahipasaoglu, S.D., Taormina, R., 2015. Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach. IEEE Trans. Cybern. doi:10.1109/TCYB.2015.2444435 (available at http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7150365&tag=1) Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A Fast and Elitist Multiobjective Genetic Algorithm. IEEE Trans. Evol. Comput. 6, 182โ197. doi:10.1109/4235.996017 Hadka, D., Reed, P., 2012. Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework. Evol. Comput. 21, 1โ30. doi:10.1162/EVCO_a_00075 Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Copyright 2015 Riccardo Taormina ([email protected]), Gulsah Karakaya ([email protected];), Stefano Galelli ([email protected]), and Selin Damla Ahipasaoglu ([email protected];. This file is part of Matlab-Multi-objective-Feature-Selection. Matlab-Multi-objective-Feature-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 Matlab-Multi-objective-Feature-Selection. If not, see <http://www.gnu.org/licenses/>.
Hi,
Thanks for sharing the code,
I am trying to run your code on my dataset.
I have 145 features extracted from 3D medical images. I have 2 questions:
The Yi Cao's Pareto-front toolbox
is not available on Mathwork. This toolbox has been removed from File Exchange. Could you please help with this?
The data that I have chosen for feature selection include the data of 2 patients' image and has almost 2 million instances. Do you think that this method of feature selection can be useful? I am going to run it my data and see the output.
Thanks
Good evening, please i have secured access from Borg MOEA, but there is nothing like toolbox. can you please refer me to any toolbox that provide the function
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