[pymvpa] the effect of ROI size on classification accuracy
brian.murphy at qub.ac.uk
Mon Jul 21 11:17:29 UTC 2014
I don't use SVMs so often, but I wonder if it is related to the setting
of the C or shrinkage parameter? With smoothing you increase the amount
of co-linearity between the input features, which can make it harder for
your algorithm to choose among features with similar informativity.
On Sun, 2014-07-20 at 17:10 +0100, Meng Liang wrote:
> Dear Jo,
> Thanks for your reply!
> I generated a series of smoothed images with Gaussian sigma from 1 mm
> to 5 mm using the same code (a for loop was used to run different
> sigma, and FSL smoothing command was used). Smoothing was done on the
> 4d nifti file directly, so I suppose it is unlikely to change the
> order of the 3d volumes. By visually inspecting the unsmoothed image
> and the smoothed image with sigma=1 mm, they look almost identical.
> The classification accuracies for all different datasets and ROIs were
> the following:
> sigma0 sigma1 sigma2 sigma3 sigma4 sigma5
> ROI1 0.7500 0.7917 0.8333 0.8750 0.8750 0.8750
> ROI2 0.7917 0.7917 0.7500 0.7500 0.6667 0.6667
> ROI3 0.7917 0.7917 0.7500 0.7500 0.6250 0.5833
> Now my impression is that it wasn't due to some mistake but smoothing
> somehow changed the distribution of the data points in the hyperspace
> in a strange way for ROI3 so that the classification accuracy was
> changed. I guess it is theorectically possible.
> If this is true, it raises another question: can we use smoothing as a
> way to test whether it is the fine-grained pattern across neiggbouring
> voxels or the very coarse pattern across different brain regions that
> drives the successful classification? The above example seems to make
> the interpretation of the results from such test a bit complicated, as
> the smoothing can have very different effect on a combined ROI (ROI3)
> than on the separate ROIs (ROI1 and ROI2). Any thoughts?
> > Date: Fri, 18 Jul 2014 16:53:54 -0500
> > From: jetzel at artsci.wustl.edu
> > To: pkg-exppsy-pymvpa at lists.alioth.debian.org
> > Subject: Re: [pymvpa] the effect of ROI size on classification
> > On 7/18/2014 12:06 PM, Meng Liang wrote:
> > > That's one reason I'm puzzled about the results. Having said that,
> > > sigma=5mm smoothing equals FWHM=11.8mm smoothing, so the smoothed
> > > image does look considerably smoother than the unsmoothed image.
> > That helps - I'm more used to thinking in FWHM. 11.8 with 2x2x2
> > is fairly substantial and likely make some sort of difference in the
> > results.
> > > I was also wondering whether this was due to some mistakes. But
> > > results were generated from the same code (the only difference is
> > > nifti image files being read into the script). Not sure what other
> > > things to check... Ideas?
> > Hmm. So you have 4d niftis with the (smoothed or not) functional
> > plus 3d niftis with the ROI masks, and just send different 4d niftis
> > the same classification code? I think you're right then to look at
> > smoothed niftis. Perhaps something went strange with the smoothing
> > procedure, say resulting in some sort of reordering? You could try
> > something like running the images through the smoothing code, but
> > zero (or nearly zero) smoothing, which shouldn't change the actual
> > functional data, to see if it turns up anything weird (i.e. if the
> > zero-smoothed images don't exactly match the before-smoothing
> > Jo
> > --
> > Joset A. Etzel, Ph.D.
> > Research Analyst
> > Cognitive Control & Psychopathology Lab
> > Washington University in St. Louis
> > http://mvpa.blogspot.com/
> > _______________________________________________
> > Pkg-ExpPsy-PyMVPA mailing list
> > Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
Dr. Brian Murphy
Lecturer (Assistant Professor)
Knowledge & Data Engineering (EEECS)
Queen's University Belfast
brian.murphy at qub.ac.uk
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