On Jul 30, 2014, at 12:24 AM, Richard Dinga <[email protected]> wrote:
> I have a question in regards of feature selection if more than one classifier > is involved, because there are more than two classes. If I understand it > correctly, in multi-class problem PyMVPA will train a classifier for every > possible pair of classes and result is decided by vote. So if I select > beforehand 100 best voxels by anova, all the classifiers would be trained on > them and there is possibility that in this subset wouldn't be informative > voxels for all the classes. How can I do it in a way that every classifier > would choose best voxels for it's own pair of classes? You could do nested crossvalidation. An example is here: http://dev.pymvpa.org/examples/nested_cv.html and a paper describing the importance of nesting is here: http://nilab.cimec.unitn.it/people/olivetti/work/papers/olivetti2010brain.pdf > > And related question: lets say I have 8 classes and I created tree like this > clf = TreeClassifier(FeatureSelectionClassifier(LinearCSVMC(), fsel), > {'a': ((1,2), LinearCSVMC()), > 'b': ((3,4), LinearCSVMC()), > 'c': ((5,6), LinearCSVMC()), > 'd': ((7,8), LinearCSVMC())}) > > The first classifier would select best voxels for dividing in 8 or 4 classes? Into four classes. > And on which voxels would be the secondary classifiers trained? I'm pretty sure it would use /all/ voxels (features). _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
