Yaroslav Halchenko wrote:
and the accuracy substantially decreased (may be this effect could
be "undone" with correlation stability analysis before
classification).
So, you did binary classification, right?
yes binary classification, using LinearNuSVMC(nu=0.5, probability=0)
was it full brain or some ROI? what # of voxels?
full brain with 34000 features
TR was 2 or 3 seconds so changing duration from 6 to 7 added another
sample into 'spatio-temporal' pattern, right?
right. TR is 2 sec. so one-(half) samlpe (+features) is added
Depending on the
classifier, the effect of substantial increase of number of feature
could have confused it, unless you've used some classifier with feature
selection... so what classifier was it (family/parameters/etc
I did some classifications with features selection (stability analysis;
# features = 5000) and the results with event duration of 7 sec. are
still worth compared to the results with (true) event duration of 6 sec.
I'll to some further classifications (with fewer voxels) and will report.
Best regards,
Matthias
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