To clarify a bit: what are your features for the classification? I take
it they're not voxel values/imaging data but rather some sort of
behavioral measures?
As a general strategy I'd work hard to make sure the feature you don't
want driving the classification is not present in the training data,
rather than trying to adjust for it afterwords.
Jo
On 7/11/2011 3:39 PM, John Clithero wrote:
Hi PyMVPAers -
I have a bit of a thought problem (but also hopefully an implementation
one):
I am performing cross-participant classification (do they belong to
group A or group B?) and that classification works quite well (I've
tried several different algorithms and the leave-one-out CVs are all
significant). However, there is a trait X that we wish to control for
(using the trait X - which is something we are not interested in and
would prefer to have no effect on prediction - as a univariate
predictor, it also performs significantly well for predicting group A or
group B in a simple logistic regression), and I am hoping for some help
in determining the best option.
One option that I've thought of involves running SVM regression on the
residuals from the logistic regression (so, instead of SVM on 0s and 1s,
give it the continuous variable of the residuals and run SVM
regression). This would (I think) effectively ask if a multivariate
analysis can predict the variance that remains in individual binary
classification after we have accounted for trait X. Does this sound
reasonable, or can an option be thought of to adjust CV post-hoc that
takes trait X into account?
And, if that does sound reasonable, is there a straightforward way to
implement this test in PyMVPA?
Thanks for humoring me and my thought problem.
Cheers,
John
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