[Pkg-exppsy-maintainers] Q: Crossvalidation feature selection

[Yaroslav Halchenko]

Yesterday git push --mirror did bad favor for us and removed some
branches (eg {mh,per}/master) from our 'central' git repository. Michael
already pushed his back into the repository, could you Per push yours?

On Sat, 29 Dec 2007, Per B. Sederberg wrote:

> Hellooo Yarik!

> On Dec 24, 2007 4:08 PM, Yaroslav Halchenko <debian at onerussian.com> wrote:
> > Hi Per,

> > And Merry Christmass!


> And a Merry X-masses to you, too!  You have sent me a great present :)

> I've prepared my datasets and am ready to try all this out.  I've
> updated to the latest version of pymvpa and I'll let you know how it
> goes.

> Latros,
> P

> > > > In any case, Michael  would correct me if I am wrong, by now we didn't
> > > > yet have a Classifier which would do some feature selection, ie you had
> > > > to implement loop through the splits manually and run RFE
> > > > FeatureSelection (using some SensitivityAnalyzer such as OnewayAnova or
> > > > LinearSVMWeights if you use SVM) on each split manually.
> > > OK, I can certainly do that loop and keep track of the results myself.
> > >  I just thought some version may already be there (and I see it may be
> > > soon :))
> > oki doki -- it is accomplished to some degree ;-)

> > I really hate naming of classifiers we have now, so a bit of refactoring
> > will be needed to make them sane. If you have any suggestion -- please
> > don't hesitate to share

> > Ok -- we have few new classifiers and sensitivity selections. First let
> > me describe FeatureSelection's:

> > SensitivityBasedFeatureSelection -- just basic one, alike 1 step RFE --
> >    ie it just removes some features based on results of some
> >    SensitivityAnalyzer.

> > FeatureSelectionPipeline -- which is to
> >   apply a list of FeatureSelection algorithms (like first remove 50% of
> >   silent according to ANOVA, and do SVM-sensitivity based RFE on the
> >   rest))

> > Now we come to the classifier you wanted:

> > FeatureSelectionClassifier -- given a base classifier and
> >   FeatureSelection (like one from above) - create a classifier which
> >   first does feature selection and then trains, and predicts using only
> >   selected features.


> > I've done just basic testing, so it still might have bugs. Also I really
> > want to discuss with Michael and maybe to come up with some better
> > naming convention.  May be simply shorten the names of the classes: like
> > every Classifier-derived class simply has Clf suffix, the same for
> > FeatureSelection to have FS, etc.

> > > I'm working on an analysis right now that could easily be generalized
> > > into an example once this classifier is in there.  Let me know when
> > > you want me to try it out.
> > please do ;-)




> > --
> > Yaroslav Halchenko
> > Research Assistant, Psychology Department, Rutgers-Newark
> > Student  Ph.D. @ CS Dept. NJIT
> > Office: (973) 353-5440x263 | FWD: 82823 | Fax: (973) 353-1171
> >         101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102
> > WWW:     http://www.linkedin.com/in/yarik


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-- 
Yaroslav Halchenko
Research Assistant, Psychology Department, Rutgers-Newark
Student  Ph.D. @ CS Dept. NJIT
Office: (973) 353-5440x263 | FWD: 82823 | Fax: (973) 353-1171
        101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102
WWW:     http://www.linkedin.com/in/yarik