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.
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>>
I have an error as below
What shall I do ?
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??? Error using ==> eval
Undefined function or variable 'data'.
Error in ==> DataLoadAndPreprocess at 26
[NPatterns, KInitialFeatures] = eval(['size(' DatasetToUse ')']);
Error in ==> DEMO>OpenDataFile_ClickedCallback at 64
[Patterns, Targets] = DataLoadAndPreprocess(handles.file);
Error in ==> gui_mainfcn at 96
feval(varargin{:});
Error in ==> DEMO at 19
gui_mainfcn(gui_State, varargin{:});
??? Error while evaluating uipushtool ClickedCallback
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