[pymvpa] Sensitivity analysis

Roberto Guidotti robbenson18 at gmail.com
Tue Jun 14 20:59:42 UTC 2011


2011/6/14 Yaroslav Halchenko <debian at onerussian.com>

> Hi Roberto, sorry for the delays with replies -- our other baby,
> NeuroDebian, urgently needing our attention ;)
>
> I'm sorry, but I supposed that my post without [pymvpa] tag were
automatically refused!! :P

Take care about your baby!! It's vital!



> >    1) In PyMVPA we have a sensitivity analyzer for each classifier which
> >    gives us the importance of dataset features, in the form of a vector
> of
> >    #feature values. These values indicates if a feature is enrolled in
> the
> >    classification task, but not if a feature is more sensitive to a class
> >    rather than others. Is there a procedure to understand this or I'm
> >    misunderstanding sensitivity analysis?
>
> well, in general it is correct, especially for binary classifiers.
>  Moreover,
> those sensitivities for linear classifiers most often are just coefficients
> of
> the separating hyperplane.  Thus they have no notion of 'class' but rather
> hint
> on importance of that feature for discrimination between participating
> classes;
> thus cannot be univocaly attributed to one or another class.  Depending on
> preprocessing, and what actual data you give for classification, the sign
> of such coefficient might be indicative of favoring higher values (higher
> activation) for one class than another in a specific feature.
>

So, If have a voxel important in my face/object task, it is used for this
discrimination. Thank you!



>
> >    2) Do you know some papers/lectures/book chapter/ books where can I
> >    learn how to understand classifier feature importance? not only in
> >    neuroimaging analysis but in general.
>
> Well, someone might recommend some nice overview on variable
> importance/sensitivity estimations.  Otherwise there are short of too many
> papers/methods.
>
> As for SVMs you might like to have a look at
>
> @Article{ Rakotomamonjy03,
>    Author = "A. Rakotomamonjy",
>    Title = "Variable Selection Using {SVM}-based Criteria",
>    Journal = "Journal of Machine Learning Research",
>    Volume = "3",
>    Pages = "1357--1370",
>    bibsource = "DBLP, http://dblp.uni-trier.de",
>    ee = "http://www.jmlr.org/papers/v3/rakotomamonjy03a.html",
>    year = 2003,
>    keywords = "Support Vector Machines",
>    url = "
> http://www.jmlr.org/papers/volume3/rakotomamonjy03a/rakotomamonjy03a.pdf"
> }
>
> which provides few approaches to features ranking in SVMs.
>
> Also of interest might be
>
> Kienzle, W., Franz, M. O., Schölkopf, B. & Wichmann, F. A. (In press).
> Center-surround patterns emerge as optimal predictors for human saccade
> targets. Journal of Vision.
>    This paper offers an approach to make sense out of feature sensitivities
> of non-linear classifiers.
>
> Sato, J. R., Mourão-Miranda, J., Martin, M. d. G. M., Amaro, E., Morettin,
> P. A. & Brammer, M. J. (2008). The impact of functional connectivity changes
> on support vector machines mapping of fMRI data. Journal of Neuroscience
> Methods, 172, 94–104.
>    Discussion of possible scenarios where univariate and multivariate (SVM)
> sensitivity maps derived from the same dataset could differ. Including the
> case were univariate methods would assign a substantially larger score to
> some features.
>    DOI: http://dx.doi.org/10.1016/j.jneumeth.2008.04.008
>
>
>
Thank you Yaroslav! You're great!!



> --
> =------------------------------------------------------------------=
> Keep in touch                                     www.onerussian.com
> Yaroslav Halchenko                 www.ohloh.net/accounts/yarikoptic
>
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