diff --git a/Concrete_Data.csv b/Concrete_Data.csv
new file mode 100644
index 0000000..b8de8ad
--- /dev/null
+++ b/Concrete_Data.csv
@@ -0,0 +1,1032 @@
+CEMENT,BFS,FLYASH,WATER,SUPERPLAST,COARSE,FINE,AGE,STRENGTH
+0,0,0,0,0,0,0,0,0.000
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+198.6 ,132.4 ,0.0 ,192.0 ,0.0 ,978.4 ,825.5 ,28 ,28.02
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+427.5 ,47.5 ,0.0 ,228.0 ,0.0 ,932.0 ,594.0 ,7 ,35.08
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+153.1 ,145.0 ,113.0 ,178.5 ,8.0 ,1001.9 ,688.7 ,28 ,25.56
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+261.9 ,110.5 ,86.1 ,195.4 ,5.0 ,895.2 ,732.6 ,28 ,33.72
+158.4 ,0.0 ,194.9 ,219.7 ,11.0 ,897.7 ,712.9 ,28 ,8.54
+150.7 ,0.0 ,185.3 ,166.7 ,15.6 ,1074.5 ,678.0 ,28 ,13.46
+272.6 ,0.0 ,89.6 ,198.7 ,10.6 ,931.3 ,762.2 ,28 ,32.25
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+143.0 ,169.4 ,142.7 ,190.7 ,8.4 ,967.4 ,643.5 ,28 ,29.73
+259.9 ,100.6 ,78.4 ,170.6 ,10.4 ,935.7 ,762.9 ,28 ,49.77
+312.9 ,160.5 ,0.0 ,177.6 ,9.6 ,916.6 ,759.5 ,28 ,52.45
+284.0 ,119.7 ,0.0 ,168.3 ,7.2 ,970.4 ,794.2 ,28 ,40.93
+336.5 ,0.0 ,0.0 ,181.9 ,3.4 ,985.8 ,816.8 ,28 ,44.87
+144.8 ,0.0 ,133.6 ,180.8 ,11.1 ,979.5 ,811.5 ,28 ,13.20
+150.0 ,236.8 ,0.0 ,173.8 ,11.9 ,1069.3 ,674.8 ,28 ,37.43
+143.7 ,170.2 ,132.6 ,191.6 ,8.5 ,814.1 ,805.3 ,28 ,29.87
+330.5 ,169.6 ,0.0 ,194.9 ,8.1 ,811.0 ,802.3 ,28 ,56.62
+154.8 ,0.0 ,142.8 ,193.3 ,9.1 ,1047.4 ,696.7 ,28 ,12.46
+154.8 ,183.4 ,0.0 ,193.3 ,9.1 ,877.2 ,867.7 ,28 ,23.79
+134.7 ,0.0 ,165.7 ,180.2 ,10.0 ,961.0 ,804.9 ,28 ,13.29
+266.2 ,112.3 ,87.5 ,177.9 ,10.4 ,909.7 ,744.5 ,28 ,39.42
+314.0 ,145.3 ,113.2 ,178.9 ,8.0 ,869.1 ,690.2 ,28 ,46.23
+312.7 ,144.7 ,0.0 ,127.3 ,8.0 ,999.7 ,822.2 ,28 ,44.52
+145.7 ,172.6 ,0.0 ,181.9 ,3.4 ,985.8 ,816.8 ,28 ,23.74
+143.8 ,136.3 ,106.2 ,178.1 ,7.5 ,941.5 ,774.3 ,28 ,26.15
+148.1 ,0.0 ,182.1 ,181.4 ,15.0 ,838.9 ,884.3 ,28 ,15.53
+277.0 ,116.8 ,91.0 ,190.6 ,7.0 ,946.5 ,665.6 ,28 ,43.58
+298.1 ,0.0 ,107.5 ,163.6 ,12.8 ,953.2 ,784.0 ,28 ,35.87
+313.3 ,145.0 ,0.0 ,178.5 ,8.0 ,1001.9 ,688.7 ,28 ,41.05
+155.2 ,183.9 ,143.2 ,193.8 ,9.2 ,879.6 ,698.5 ,28 ,28.99
+289.0 ,133.7 ,0.0 ,194.9 ,5.5 ,924.1 ,760.1 ,28 ,46.25
+147.8 ,175.1 ,0.0 ,171.2 ,2.2 ,1000.0 ,828.5 ,28 ,26.92
+145.4 ,0.0 ,178.9 ,201.7 ,7.8 ,824.0 ,868.7 ,28 ,10.54
+312.7 ,0.0 ,0.0 ,178.1 ,8.0 ,999.7 ,822.2 ,28 ,25.10
+136.4 ,161.6 ,125.8 ,171.6 ,10.4 ,922.6 ,764.4 ,28 ,29.07
+154.8 ,0.0 ,142.8 ,193.3 ,9.1 ,877.2 ,867.7 ,28 ,9.74
+255.3 ,98.8 ,77.0 ,188.6 ,6.5 ,919.0 ,749.3 ,28 ,33.80
+272.8 ,105.1 ,81.8 ,209.7 ,9.0 ,904.0 ,679.7 ,28 ,37.17
+162.0 ,190.1 ,148.1 ,178.8 ,18.8 ,838.1 ,741.4 ,28 ,33.76
+153.6 ,144.2 ,112.3 ,220.1 ,10.1 ,923.2 ,657.9 ,28 ,16.50
+146.5 ,114.6 ,89.3 ,201.9 ,8.8 ,860.0 ,829.5 ,28 ,19.99
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+143.6 ,0.0 ,174.9 ,158.4 ,17.9 ,942.7 ,844.5 ,28 ,15.42
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+304.8 ,0.0 ,99.6 ,196.0 ,9.8 ,959.4 ,705.2 ,28 ,30.12
+150.9 ,0.0 ,183.9 ,166.6 ,11.6 ,991.2 ,772.2 ,28 ,15.57
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+366.0 ,187.0 ,0.0 ,191.3 ,6.6 ,824.3 ,756.9 ,28 ,65.91
+279.8 ,128.9 ,100.4 ,172.4 ,9.5 ,825.1 ,804.9 ,28 ,52.83
+252.1 ,97.1 ,75.6 ,193.8 ,8.3 ,835.5 ,821.4 ,28 ,33.40
+164.6 ,0.0 ,150.4 ,181.6 ,11.7 ,1023.3 ,728.9 ,28 ,18.03
+155.6 ,243.5 ,0.0 ,180.3 ,10.7 ,1022.0 ,697.7 ,28 ,37.36
+160.2 ,188.0 ,146.4 ,203.2 ,11.3 ,828.7 ,709.7 ,28 ,35.31
+298.1 ,0.0 ,107.0 ,186.4 ,6.1 ,879.0 ,815.2 ,28 ,42.64
+317.9 ,0.0 ,126.5 ,209.7 ,5.7 ,860.5 ,736.6 ,28 ,40.06
+287.3 ,120.5 ,93.9 ,187.6 ,9.2 ,904.4 ,695.9 ,28 ,43.80
+325.6 ,166.4 ,0.0 ,174.0 ,8.9 ,881.6 ,790.0 ,28 ,61.24
+355.9 ,0.0 ,141.6 ,193.3 ,11.0 ,801.4 ,778.4 ,28 ,40.87
+132.0 ,206.5 ,160.9 ,178.9 ,5.5 ,866.9 ,735.6 ,28 ,33.31
+322.5 ,148.6 ,0.0 ,185.8 ,8.5 ,951.0 ,709.5 ,28 ,52.43
+164.2 ,0.0 ,200.1 ,181.2 ,12.6 ,849.3 ,846.0 ,28 ,15.09
+313.8 ,0.0 ,112.6 ,169.9 ,10.1 ,925.3 ,782.9 ,28 ,38.46
+321.4 ,0.0 ,127.9 ,182.5 ,11.5 ,870.1 ,779.7 ,28 ,37.27
+139.7 ,163.9 ,127.7 ,236.7 ,5.8 ,868.6 ,655.6 ,28 ,35.23
+288.4 ,121.0 ,0.0 ,177.4 ,7.0 ,907.9 ,829.5 ,28 ,42.14
+298.2 ,0.0 ,107.0 ,209.7 ,11.1 ,879.6 ,744.2 ,28 ,31.88
+264.5 ,111.0 ,86.5 ,195.5 ,5.9 ,832.6 ,790.4 ,28 ,41.54
+159.8 ,250.0 ,0.0 ,168.4 ,12.2 ,1049.3 ,688.2 ,28 ,39.46
+166.0 ,259.7 ,0.0 ,183.2 ,12.7 ,858.8 ,826.8 ,28 ,37.92
+276.4 ,116.0 ,90.3 ,179.6 ,8.9 ,870.1 ,768.3 ,28 ,44.28
+322.2 ,0.0 ,115.6 ,196.0 ,10.4 ,817.9 ,813.4 ,28 ,31.18
+148.5 ,139.4 ,108.6 ,192.7 ,6.1 ,892.4 ,780.0 ,28 ,23.70
+159.1 ,186.7 ,0.0 ,175.6 ,11.3 ,989.6 ,788.9 ,28 ,32.77
+260.9 ,100.5 ,78.3 ,200.6 ,8.6 ,864.5 ,761.5 ,28 ,32.40
diff --git a/ELMregression.m b/ELMregression.m
new file mode 100644
index 0000000..be74dc0
--- /dev/null
+++ b/ELMregression.m
@@ -0,0 +1,74 @@
+function [trYhat, valYhat,W1,W2,bias] =...
+ ELMregression(trX, trY, valX, nUnits)
+
+% This function implements an ELM classifier with tanh activation function.
+%
+% Inputs: trX <- array of training inputs with size = num. features x num. training patterns
+% trY <- array of training targets with size = num. categories x num. training patterns
+% (for each i-th column of trY only the entry relative to the correct category is 1)
+% valX <- array of validation inputs with size = num. features x num. training patterns
+% nUnits <- num. hidden units of ELM
+%
+% Output:
+% trYhat <- array of training target predictions with size = 1 x num. training patterns
+% (each i-th is an integer = predicted category)
+% valYhat <- array of validaiton target predictions with size = 1 x num. validation patterns
+% (each i-th is an integer = predicted category)
+% W1,W2,bias <- the trained parameters of the ELM
+%
+% Reference: Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: Theory and applications.
+% Neurocomputing 70, 489–501. doi:10.1016/j.neucom.2005.12.126
+%
+%
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+%
+
+% get number of features and number of patterns for training and validation
+[nFeatures,nPatternsTr] = size(trX);
+nPatternsVal = size(valX,2);
+
+% generate random input->hidden weights W1 (between -1 and 1)
+W1 = rand(nUnits,nFeatures)*2-1;
+
+% generate random biases (between 0 and 1)
+bias = rand(nUnits,1);
+
+% compute hidden neuron output matrix H
+H = sigActFun(W1*trX + repmat(bias,[1,nPatternsTr]));
+
+% compute hidden->output weights W2
+Hinv = pinv(H');
+W2 = Hinv * trY';
+
+% get ELM response on training
+temp = (H' * W2)';
+[~,temp] = max(temp,[],1);
+trYhat = temp';
+
+% ... and validation dataset
+Hval = sigActFun(W1*valX + repmat(bias,[1,nPatternsVal]));
+valYhat = (Hval' * W2)';
diff --git a/computeSU.m b/computeSU.m
new file mode 100644
index 0000000..4d69ad3
--- /dev/null
+++ b/computeSU.m
@@ -0,0 +1,48 @@
+function SU = computeSU(x,y)
+% Computes simmetric uncertainty between two variables
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+
+% discretization options
+nBins = 20;
+quantType = 'equalwidth';
+
+% quantize variables
+x = quantizeVariable(x,nBins,quantType);
+y = quantizeVariable(y,nBins,quantType);
+
+% compute entropies
+hX = entropy(x);
+hy = entropy(y);
+hXy = jointentropy(x, y);
+
+% compute mutual information
+MI = hX+hy-hXy;
+
+% compute symmetric uncertainty
+SU = 2*MI/(hX+hy);
+
+
diff --git a/getAlgorithmOptions.m b/getAlgorithmOptions.m
index c3ceda5..baa2a2a 100644
--- a/getAlgorithmOptions.m
+++ b/getAlgorithmOptions.m
@@ -1,9 +1,9 @@
-function [options,objFunOptions] = getAlgorithmOptions(algorithm,data)
+function [options,objFunOptions] = getAlgorithmOptions(algorithm,data,varargin)
% Options for the algorithms (NSGAII/BORG) and the objective function
%
%
%
-% Copyright 2015 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
% Stefano Galelli (stefano_galelli@sutd.edu.sg),
% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
@@ -28,29 +28,44 @@
% If not, see .
%
+% check nargin
+if nargin == 2
+ problem_type = 'CLASSIFICATION';
+elseif (nargin == 3) && varargin{1} == true
+ problem_type = 'REGRESSION';
+else
+ error('Problem type not recognized!')
+end
+
+
+
% extract attributes (PHI) and predictand (Y)
PHI = data(:,1:end-1);
[nPatterns,nAttrs] = size(PHI);
tempY = data(:,end);
-% restructure predictand (array with same number of columns of number of classes)
-classes = unique(tempY);
-nClasses = numel(classes);
-Y = zeros(nPatterns,nClasses);
-for i = 1 : nClasses
- thisClass = classes(i);
- ixes = (tempY == thisClass);
- Y(ixes,i) = 1;
+if strcmp(problem_type, 'CLASSIFICATION')
+ % restructure predictand (array with same number of columns of number of classes)
+ classes = unique(tempY);
+ nClasses = numel(classes);
+ Y = zeros(nPatterns,nClasses);
+ for i = 1 : nClasses
+ thisClass = classes(i);
+ ixes = (tempY == thisClass);
+ Y(ixes,i) = 1;
+ end
+else
+ Y = tempY;
end
% Objective Function options
-objFunOptions.Y = Y; % predictand
-objFunOptions.PHI = PHI; % attributes
-objFunOptions.nFolds = 10; % folds for k-fold cross-validation
-objFunOptions.nELM = 5; % size of ELM ensemble
-objFunOptions.nUnits = 10; % number of units in ELM
-objFunOptions.maxCardinality = 20; % maximum cardinality (important for large datasets)
+objFunOptions.Y = Y; % predictand
+objFunOptions.PHI = PHI; % attributes
+objFunOptions.nFolds = 10; % folds for k-fold cross-validation
+objFunOptions.nELM = 10; % size of ELM ensemble
+objFunOptions.nUnits = 50; % number of units in ELM
+objFunOptions.maxCardinality = 20; % maximum cardinality (important for large datasets)
% Algorithm options
if strcmp(algorithm,'NSGA2')
@@ -73,7 +88,7 @@
options.nvars = nAttrs; % number of design variables
options.nconstrs = 0; % number of contraints
options.NFE = 5000; % number of functions evaluations
- options.lowerBounds = zeros(1,nAttrs); % lower bound of design variables (0)
+ options.lowerBounds = -ones(1,nAttrs); % lower bound of design variables (-1)
options.upperBounds = ones(1,nAttrs); % upper bound of design variables (1)
else
error('Algorithm not supported!')
diff --git a/objFunWQEISS_regression.m b/objFunWQEISS_regression.m
new file mode 100644
index 0000000..f8bc1bb
--- /dev/null
+++ b/objFunWQEISS_regression.m
@@ -0,0 +1,108 @@
+function [fval,dummy] = objFunWQEISS_regression(X,varargin)
+global archive fvals objFunOptions suREL suRED ix_solutions
+% objective function for developing WQEISS wrappers
+%
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+%
+
+
+% initialize fitness values
+fval = zeros(1,4);
+
+% unpack data and parameters
+Y = objFunOptions.Y; % targets
+PHI = objFunOptions.PHI; % inputs
+nFolds = objFunOptions.nFolds; % nFolds for k-fold cross validation
+nELM = objFunOptions.nELM; % number of repeats for computing the accuracy obj function
+nUnits = objFunOptions.nUnits; % info on dataset
+maxCardinality = objFunOptions.maxCardinality; % maximum cardinality
+
+% retrieve populations size and number of attributes
+nAttrs = size(X,2);
+
+% transform decision variables from continuous to discrete
+% 0 or 1 assigned depending on ratio of maxCardinality/nAttrs
+% (This has no effect if the search algorithm is binary-coded already!)
+varRatio = maxCardinality/nAttrs;
+if varRatio > 0.5
+ X = X>0.5;
+else
+ X = X>(1 - varRatio);
+end
+
+% get selected features from genotype
+featIxes = find(X);
+
+% get cardinality
+cardinality = numel(featIxes);
+
+
+% check if this combination of inputs is already in archive
+% if so, assign existing fitness values to this genotype
+temp = cellfun(@(x) isequal(x,featIxes),archive,'UniformOutput',false);
+archiveIx = find([temp{:}]);
+if ~isempty(archiveIx);
+ % get fval from lookup table
+ fval = fvals(archiveIx,:);
+ ix_solutions(archiveIx) = 1;
+else
+ if cardinality > maxCardinality
+ % if cardinality > maxCardinality do not evaluate and assign very
+ % high values of the obj functions
+ fval = [Inf,Inf,Inf,numel(featIxes)];
+ elseif cardinality == 0
+ % no inputs selected, irregular solution
+ fval = [Inf,Inf,Inf,numel(featIxes)];
+ else
+ % found new combination, compute values of obj. functions
+
+ % relevance
+ REL = sum(suREL(featIxes));
+
+ % redundancy
+ if cardinality == 1
+ % 1 input selected, no redundancy
+ RED = 0;
+ else
+ temp = nchoosek(featIxes,2);
+ ixes = (temp(:,2)-1)*nAttrs+temp(:,1);
+ RED = sum(suRED(ixes));
+ end
+
+ % compute ELM classifier accuracy
+ SU = trainAndValidateELM_regression(PHI,Y,featIxes,nFolds,nELM,nUnits);
+
+ % fitness values (- for those obj. functions to maximize)
+ fval = [-REL,RED,-SU,cardinality];
+ % add solution to archive and fvals
+ archive = cat(1,archive,featIxes);
+ fvals = cat(1,fvals,[-REL,RED,-SU,cardinality]);
+ ix_solutions = cat(1,ix_solutions,1);
+ end
+end
+
+dummy = [];
\ No newline at end of file
diff --git a/script_example_BORG__REGRESSION.m b/script_example_BORG__REGRESSION.m
new file mode 100644
index 0000000..c9e9cb3
--- /dev/null
+++ b/script_example_BORG__REGRESSION.m
@@ -0,0 +1,134 @@
+% This script illustrates the Borg implementation of the
+% WQEISS input selection technique described in:
+%
+% Taormina, R., Galelli, S., Karakaya, G., Ahipasaoglu, S.D.
+% An information theoretic approach to select alternate subsets
+% of predictors for data-driven hydrological models.
+% Water Resources Research (in review)
+%
+% WQEISS and other techniques for feature selection in classificatio
+% problems are described in:
+%
+% 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
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+
+clc; clear;
+
+%% specify include paths
+addpath('..\..\Work\Code\toolboxes\mi'); % Peng's mutual information
+addpath('..\..\Work\Code\toolboxes\borg-matlab\'); % Borg
+addpath('..\..\Work\Code\toolboxes\paretofront'); % paretofront toolbox
+
+
+%% Load and prepare dataset
+
+% load dataset
+filePath = 'Concrete_Data.csv';
+[orig_data,varNames,varTypes] = readData(filePath);
+
+% transform data
+transf_data = transformData(orig_data,varTypes);
+
+% normalize data
+norm_data = normalizeData(transf_data);
+
+% compute relevance and redundacy
+global suRED suREL
+[suRED,suREL] = computeRelevanceRedundancy(norm_data);
+
+
+%% Prepare for launching the algorithms
+
+% specify GO algorithm to use (BORG or NSGA2)
+GOalgorithm = 'BORG';
+
+% get algorithm options
+global objFunOptions
+
+[options,objFunOptions] = ...
+ getAlgorithmOptions(GOalgorithm,norm_data,true);
+
+% initialize overall archive and array containing the values of the
+% objctive functions (fvals)
+global archive fvals ix_solutions
+archive = {}; % archive of all solutions explored
+fvals = []; % values of the obj function explored
+ % RELEVANCE - REDUNDACY - SU - #INPUTS
+
+ix_solutions = []; % this will track which solutions are found by each algorithm
+
+
+%% launch WQEISS
+fprintf ('Launching WQEISS\n')
+
+% define number of obj functions and the matlab function coding them
+options.nobjs = 4;
+options.objectiveFcn = @objFunWQEISS_regression;
+epsilon = 10^-3;
+epsilons = repmat(epsilon, [1,options.nobjs]);
+
+% launch
+borg(...
+ options.nvars,options.nobjs,options.nconstrs,...
+ options.objectiveFcn, options.NFE,...
+ options.lowerBounds, options.upperBounds, epsilons);
+
+
+% get solutions indexes for WQEISS
+ixWQEISS = find(ix_solutions);
+
+
+% compute final pareto front
+ixesPF = find(paretofront(fvals(ixWQEISS,:)));
+PF_WQEISS.archive = archive(ixWQEISS(ixesPF));
+PF_WQEISS.fvals = fvals(ixWQEISS(ixesPF),:);
+PF_WQEISS.fvals_ext = fvals(ixWQEISS(ixesPF),:);
+
+
+
+
+%% delta elimination
+delta = 20;
+PFdelta_WQEISS = deltaElimination(PF_WQEISS,delta);
+
+%% Plot Frequency matrices
+figure('name','W-QEISS frequency matrices');
+plotFrequencyMatrix(PFdelta_WQEISS,options.nvars,varNames)
+
+
+
+
+
+
+
+
+
+
+
diff --git a/script_example_NSGAII__REGRESSION.m b/script_example_NSGAII__REGRESSION.m
new file mode 100644
index 0000000..ee1a566
--- /dev/null
+++ b/script_example_NSGAII__REGRESSION.m
@@ -0,0 +1,132 @@
+% This script illustrates the NSGA-II implementation of the
+% WQEISS input selection technique described in:
+%
+% Taormina, R., Galelli, S., Karakaya, G., Ahipasaoglu, S.D.
+% An information theoretic approach to select alternate subsets
+% of predictors for data-driven hydrological models.
+% Water Resources Research (in review)
+%
+% WQEISS and other techniques for feature selection in classificatio
+% problems are described in:
+%
+% 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
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+
+
+clc; clear;
+
+%% include paths
+addpath('..\..\Work\Code\toolboxes\mi'); % Peng's mutual information
+addpath('..\..\Work\Code\toolboxes\nsga2_MATLAB_alternative'); % LIN's NPGM (for NSGA-II)
+addpath('..\..\Work\Code\toolboxes\paretofront'); % Yi Cao's paretofront toolbox
+
+
+
+%% Load and prepare dataset
+
+% load dataset
+filePath = 'Concrete_Data.csv';
+[orig_data,varNames,varTypes] = readData(filePath);
+
+% remove rainfall column from ForestFires dataset
+% (sometimes it leads to singularity problems)
+% orig_data(:,end-1) = [];
+
+% transform data
+transf_data = transformData(orig_data,varTypes);
+
+% normalize data
+norm_data = normalizeData(transf_data);
+
+% compute relevance and redundacy
+global suRED suREL
+[suRED,suREL] = computeRelevanceRedundancy(norm_data);
+
+
+%% Prepare for launching the algorithms
+
+% specify GO algorithm to use (BORG or NSGA2)
+GOalgorithm = 'NSGA2';
+
+% get algorithm options
+global objFunOptions
+
+[options,objFunOptions] = ...
+ getAlgorithmOptions(GOalgorithm,norm_data,true);
+
+% initialize overall archive and array containing the values of the
+% objctive functions (fvals)
+global archive fvals ix_solutions
+archive = {}; % archive of all solutions explored
+fvals = []; % values of the obj function explored
+ % RELEVANCE - REDUNDACY - SU - #INPUTS
+
+ix_solutions = []; % this will track which solutions are found by each algorithm
+
+%% launch WQEISS
+fprintf ('Launching WQEISS\n')
+
+% define number of obj functions and the matlab function coding them
+options.numObj = 4;
+options.objfun = @objFunWQEISS_regression;
+
+% launch
+nsga2(options);
+
+% get solutions indexes for WQEISS
+ixWQEISS = find(ix_solutions);
+
+
+% compute final pareto front
+ixesPF = find(paretofront(fvals(ixWQEISS,:)));
+PF_WQEISS.archive = archive(ixWQEISS(ixesPF));
+PF_WQEISS.fvals = fvals(ixWQEISS(ixesPF),:);
+PF_WQEISS.fvals_ext = fvals(ixWQEISS(ixesPF),:);
+
+
+
+%% delta elimination
+delta = 100;
+PFdelta_WQEISS = deltaElimination(PF_WQEISS,delta);
+
+%% Plot Frequency matrices
+figure('name','W-QEISS frequency matrices');
+plotFrequencyMatrix(PFdelta_WQEISS,options.numVar,varNames)
+
+
+
+
+
+
+
+
+
+
+
diff --git a/trainAndValidateELM_regression.m b/trainAndValidateELM_regression.m
new file mode 100644
index 0000000..df4c36e
--- /dev/null
+++ b/trainAndValidateELM_regression.m
@@ -0,0 +1,74 @@
+function SU = trainAndValidateELM_regression(PHI,Y,featIxes,nFolds,nELM,nUnits)
+% This function trains and validate an ELM classifier with k-fold
+% cross-validation
+%
+% Inputs: PHI <- array of training inputs with size = num. patterns x num. features
+% Y <- array of training targets with size = num. patterns x num. categories
+% (for each i-th column of trY only the entry relative to the correct category is 1)
+% featIxes <- features selected (they are columns of PHI)
+% nFolds <- num. folds for cross validation
+% nELM <- num. ELM in the ensemble
+% nUnits <- num. hidden units of ELM
+%
+% Output:
+% accuracy <- accuracy of the predictions of the cross-validated ELM ensemble
+%
+%
+%
+% Copyright 2016 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
+% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
+% Stefano Galelli (stefano_galelli@sutd.edu.sg),
+% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
+%
+% Please refer to README.txt for further information.
+%
+%
+% 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_IterativeInputSelection.
+% If not, see .
+%
+
+% initialize
+Yhat = zeros(size(Y,1),1);
+SU = zeros(1,nELM) + Inf;
+for j = 1 : nELM
+
+ % k-fold cross validation
+ lData = size(Y,1);
+ lFold = floor(lData/nFolds);
+
+ for i = 1 : nFolds
+ % select trainind and validation data
+ ix1 = (i-1)*lFold+1;
+ if i == nFolds
+ ix2 = lData;
+ else
+ ix2 = i*lFold;
+ end
+ valIxes = ix1:ix2; % select the validation chunk
+ trIxes = setdiff(1:lData,valIxes); % obtain training indexes by set difference
+
+ % create datasets
+ trX = PHI(trIxes,featIxes); trY = Y(trIxes,:);
+ valX = PHI(valIxes,featIxes);
+
+ % train and test ELM
+ [~,Yhat(valIxes)] =...
+ ELMregression(trX', trY', valX', nUnits);
+ end
+
+ SU(j) = computeSU(Y,Yhat);
+end
+SU = mean(SU);
\ No newline at end of file
diff --git a/transformData.m b/transformData.m
index 53fec6d..152dc3b 100644
--- a/transformData.m
+++ b/transformData.m
@@ -35,8 +35,8 @@
%
% discretization options
-nBins = 10;
-quantType = 'equalfreq';
+nBins = 20;
+quantType = 'equalwidth';
% initialize output array
[nObs,nVars] = size(data);