Perform recursive feature elimination, followingI. Guyon, J. Weston, S. Barnhill and V. Vapnik. "Gene selection for cancer classification using support vector machines." Machine Learning. 46(1-3):389-422, 2002.This is an algorithm for selecting a subset of features for a particular learning task. The basic algorithm is the following:
- Initialize the data set to contain all features.
- Train an SVM on the data set.
- Rank features according to ci = (wi)2.
- Eliminate the lower-ranked 50% of the features.
- If more than one feature remains, return to step 2.
When using this algorithm, beware of incurring a selection bias. For details, seeC. Ambroise and G. J. McLachlan. "Selection bias in gene extraction on the basis of microarray gene-expression data." PNAS. 99:6562-6566, 2002.
gist-rfe [options] <train data>
score-svm-results) at each iteration of the SVM-RFE algorithm. By default, evaluates the performance using leave-one-out cross-validation. The
-testoption enables evaluation on an independent test set.
%, then multiple output files will be created, replacing
%with the iteration number.
In addition, any option that is valid for
may also be given to SVM-RFE.