I have some questions about 3dsvm.
I am trying to run 3dsvm for classifying multi-class groups(in my case, four groups) from task-related fMRI data.
GroupA : 30 subjects
GroupB : 40 subjects
GroupC : 28 subjects
GroupD : 32 subjects
To generate ‘TrainVol’(4D volume=3D beta images from each subject * # of subjects), should all ‘TrainVol’ from each group have the same size? I am wondering if each ‘TrainVol’ can have the maximum size of 28 Volumes since I have only 28 subjects for GroupC.
I set testlabel as 1,2,3, and 4 for group label. But the range of 3dsvm prediction result -1.xxxx to 1.xxxx. How can I calculate accuracy or specificity?
To augment data set, is it fine to make TrainVol with multiple set? For example, to make TrainVol for GroupA, I took out 5 subjects for test set. The rest of subject(25 subjects) should have been randomly ordered and I made TrainVol_GroupA_01. I took again another randomly ordered 25 subjects and I made TrainVol_GroupA_02. Finally I got 10 multiple set from TrainVol_GroupA_01 to TrainVol_GroupA_10 and I concatenated them to single 4D volume TrainVol_GroupA_Total. Is this the right way to augment data, or I don’t need to do this step?