Dimitrios Ververidis

Go to options and make confidence interval smaller. This will increase the number of cross-validation repetitions, thus execution time gets longer. The features selected will be almost identical per run. This is due to the fact that cross-validation involves a random selection of training and testing set.

Hi,

1)There is no missing end in the "DataLoadAndPreprocess.m". As regards the reduction of 114 features to 90, it is happening because there were some features with NaNs in the certain dataset that were removed.

You can remove such features (if you have NaNs of Infs in your data) by adding some lines in function DataLoadAndPreprocess.m.

2) Well, there was a minor bug:

At the ForwSel_main.mat replace lines 256 to 273 with the following:

    if ~isempty(handles)
        axes(handles.YelLinesAxes);
        hold on
        if (NPatterns > KFeatures)
            axis([0 NPatterns 0 KFeatures]); axis manual
            HYelLines(FeatureToInclude)=plot([0 NPatterns+2],...
                (FeatureToInclude-0.5)*ones(1,2),'y','linewidth',3);
        else
            axis([0 KFeatures 0 NPatterns]); axis manual
            HYelLines(FeatureToInclude)= plot(...
                FeatureToInclude*ones(1,2),[0 KFeatures+2],'y','linewidth',3);
        end
        set(gca,'Visible','off');
        drawnow
        set(findobj(gcf,'Tag','ListSelFeats'), 'String', ...
            sort(SelectedFeatPool));
        axes(handles.FeatSelCurve);
    end

3) There is no need to change the name of your data. It works with any name. For example I debugged the previous error by generating a two class problem of 200 patterns (100 per class) and 2000 features estimated on them:

>> x = [0.25+0.1*randn(100,2000); 0.35+0.25*randn(100,2000)];
>> Data = [x [ones(100,1); 2*ones(100,1)]];

That correspond to patternsXfeatures matrix 200 X 2000
and Targets vector 200 X 1

I saved the "Data" variable as "Data.mat" in the [PatTargMatrices] folder and I loaded it with the GUI and I pressed run.

I don't have the linux version of Matlab and I don't know the differences. I don't think that there is any.

BR,
Dimitrios

User can select between SFS and SFFS. See menu options. They are not combined.

They both belong to the general forward selection function. SFS is actually a part of SFFS.

With this move I avoided writing the same code twice.

Dear all,
I am new to feature selection topic. I have found that this program is useful for my data. However each time i run the program, it ended up with different answers/different features. So could anybody tell me how to choose the best features for my data.

hello all,

I have the same problem of tabzim, that when I use the following code it returns just one feature>> could you help me if you solve your problem??

cc = cvpartition(y,'k',10);
opts = statset('display','iter');
[fs,history] = sequentialfs( @fc,X,y,'cv',cc,'direction','backward','options',opts)%,'nfeatures',5)

function [sm]=fc(Xtrain,Ytrain,Xtest,Ytest)
yy=svmclassify(svmtrain(Xtrain,Ytrain),Xtest);
sm=sum(~strcmp(Ytest,yy))
-----

so please help>>