Variable Selection Using SVM-based Criteria
Alain Rakotomamonjy;
3(Mar):1357-1370, 2003.
Abstract
We propose new methods to evaluate variable subset relevance with
a view to variable selection. Relevance criteria are derived from
Support Vector Machines and are based on weight vector
||
w||
2 or generalization error bounds sensitivity with
respect to a variable. Experiments on linear and non-linear toy
problems and real-world datasets have been carried out to assess
the effectiveness of these criteria. Results show that the
criterion based on weight vector derivative achieves good results
and performs consistently well over the datasets we used.
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