Hi,
On Fri, Jan 7, 2011 at 2:10 AM, Noah Silverman <n...@smartmediacorp.com> wrote:
> I have a data set with about 30,000 training cases and 103 variable.
>
> I've trained an SVM (using the e1071 package) for a binary classifier {0,1}.
> The accuracy isn't great.
>
> I used a grid search over the C and G parameters with an RBF kernel to find
> the best settings.
>
> I remember that for least squares, R has a nice stepwise function that will
> try combining subsets of variables to find the optimal result. Clearly,
> this doesn't exist for SVMs as a built in function.
>
> As an experiment, I simply grabbed the first 50 variables and repeated the
> training/grid search procedure. The results were significantly better.
> Since the date is VERY noisy, my guess is that eliminating some of the
> variables eliminated some noise that resulted in better results.
>
> With a grid of 100 parameter settings (10 for C, 10 for G) and 106
> variables, trying every combination would be prohibitively time consuming.
>
> Can anyone suggest an approach to seek the ideal subset of variables for my
> SVM classifier?
Sounds like a job for the types of approaches found in the penalizedSVM package:
http://cran.r-project.org/web/packages/penalizedSVM/index.html
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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