Feature selection in SVM via polyhedral k-norm
Gaudioso M.
;Gorgone E.;
2020-01-01
Abstract
We treat the feature selection problem in the support vector machine (SVM) framework by adopting an optimization model based on use of the ℓ pseudo-norm. The objective is to control the number of non-zero components of the normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral norm ‖. ‖ [k], intermediate between ‖. ‖ 1 and ‖. ‖ ∞, plays a significant role, allowing us to come out with a DC (difference of convex) optimization problem that is tackled by means of DCA algorithm. The results of several numerical experiments on benchmark classification datasets are reported.File | Size | Format | |
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Gaudioso2020_Article_FeatureSelectionInSVMViaPolyhe.pdf Solo gestori archivio
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