[R] variable selection with support vector machines (SVM)
Serge Zaugg
sezaugg at gmx.ch
Tue Nov 21 15:50:12 CET 2006
Hello
I am using support vector machine (from package kernlab) for a classification task (with RBF-Kernel). My data has dozens of variables and I need to identify which variables contribute most to the classification performance.
What I did so far is comparing the classification performance (measured for example with the proportion of misclassified cases) of different sets of variables with cross-validation. Unfortunately this is very slow and doing, for example, a backward variable selection procedure will take half a day with my data.
This raises 3 interrelated questions:
Does someone know an alternative way to perform variable selection in the context of SVM-classification ?
Does someone know of an R-function that automatizes variable selection for SVM ?
Is there a way to quantify the contribution of every single variable to the classification performance ? (I guess there is no short answer to this but I would also be very happy on references of good articles or books on this topic)
Thanks a lot in advance,
Serge Zaugg
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Serge Zaugg
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sezaugg at gmx.ch
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