First upload

Added main files, dataset and NSGA2 example script
Critical-Infrastructure-Systems-Lab · Jun 21, 2015 · 775d32c · 775d32c
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diff --git a/ELMclassifier.m b/ELMclassifier.m
@@ -0,0 +1,76 @@
+function [trYhat, valYhat,W1,W2,bias] =...
+    ELMclassifier(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 2015 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 <http://www.gnu.org/licenses/>.
+%
+
+% 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 = tanhActFun(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     = tanhActFun(W1*valX + repmat(bias,[1,nPatternsVal]));
+temp     = (Hval' * W2)';
+[~,temp] = max(temp,[],1);
+valYhat  = temp';
diff --git a/Heart.csv b/Heart.csv
@@ -0,0 +1,272 @@
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