One strategy is to use multiple classification analyses and compare the
results; sort of like the 'virtual lesion' method. Using your flag
example, a classifier given only "has stripes" - the "has stars" feature
was lesioned - will fail, but one given both 'has stars' and 'has
stripes' will succeed. In the context of fMRI, classification could
succeed when an entire ROI is used, but fail when a subpart of the ROI
is omitted.
This type of strategy is not always applicable (or straightforward to
implement and interpret), but sounds to me like it might be a bit closer
to what you're trying to find out.
Jo
On 1/31/2013 8:53 AM, Roberto Guidotti wrote:
Thank you guys,
Yes, I know that I can't predict using a portion of voxel. Let's say
that I would like to train on full brain and test on a portion, putting
out of ROI voxel intensity to zero.
I don't know if it makes sense conceptually because I would like to
predict using a portion of features on a model built on multiple features.
Probably could be an sensitivity measure, e.g. building a classifier to
predict from flag the country for example Liberarian flag and stars and
stripes, If I use features from stripes part of the flag (common in both
of the flags) the classifier isn't able to classify - well, using a
feature selection probably those features were discarded - but using
"stars" part as ROI the classifier identifies the flag, and so I will
know where the classifier is more sensitive! (Hope I explained it clear).
I don't know if there is still a theoretical problem.
Thank you
R
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