I used MRMR filter approach, then run GA-SVM(Wrapper approach) on the best 100 features selected from MRMR. MrMR run discretization {0,2,-2} data, while Libsvm run into scaling data between (0,1).
Does the changing of data format will mislead the LIBSM Classifier, Since the best feature picked up on dicretization data format While the second stage (wrapper approach+classification) run into scaling data betweeen (0-1) ???
Salam Sir Alomari,
The Libsvm package proposes a matlab implementation for scaling data (link 1): (I haven't tested yet)
But, I do not see the added value of using the wrapper approach in the second stage because the best features have already been selected by the filter method.
In my opinion, You can either use libsvm as just a classifier or integrate MRMR into the wrapper approach as in the attached file (in the second case, libsvm has to be integrated into the MRMR code).
May Allah help you.
Best regards.
http://stackoverflow.com/questions/10055396/scaling-the-testing-data-for-libsvm-matlab-implementation
Walikum Salam Dr malek
I have used hybrid approaches as in first stage i used MRMR to select the best 100 features.Then, I used GA-SVM to find the goodness feature subset.
I confused in which data format should be without negative affect or mislead the classifier?
A- Should I run MRMR on scaling data then run svm on same data format??
Or
B- run MRMR on discretization data as suggested by author of MRMR then back to convert original data scaling format (0-1) ??
Is it necessary to keep same data format in feature selection and classification process?
Thanks For Cooperation
Thank you for this explanation.
I do not have much experience to suggest the best one. But, in my opinion, if the feature selection (FS) approach MRMR do not depends on the classifier (SVM), there is not a need to use the same format because the aim of FS is to optimize the MRMR criterion. The same format can be useful if we integrate SVM into MRMR as it is done in some papers.
Furthermore, you can test both of them and choose the one which gives the best classification accuracy (which is the main purpose of FS)
Hi Osama,
mRMR is a filter approach based on mutual information and GA-SVM is wrapper approach that used the classification accuracy rate as objective function.
The best way is to run mRmR first and selection the "K" best features. The next steps of your approach is used GA-SVM and select the relevant subset among the "K" best features.
To use GA-SVM, we must define a new binary variable X which has K feature and takes 0 is the feature is not selected and 1 if the feature is selected.
You can read my publication, i have used SA-SVM "Simulated annealing for feature selection"
http://www.mecs-press.org/ijitcs/ijitcs-v7-n5/IJITCS-V7-N5-1.pdf
Thanks Dr Seyyid
what that data format you used to Run MRMR on it, then how about data format on wrapper approach>\??
So, My question is the changing of data format cause information loss??
MRMR run on discretization data.
SVM accept scaling data between 0-1
Thanks for Cooperation
I do not understand why the data exchange ?
suppose that you have 5 features X = {X1, X2, X3, X4, X5}, you split the data into tow sets: learning set, test set.
you run mRmR on learning set and you pick the best features. suppose that the best features are : X1, X3, X4, X5
now, you run GA-SVM on X1, X3, X4, X5, in this stage :
the GA algorithm generate binary vector {1,0,0,1}
the SVM use the features : X1 and X2 to build the classification model.
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