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.

Feature selection in SVM via polyhedral k-norm

Gaudioso M.;Gorgone E.;
2020

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.
Cardinality constraint; DC optimization; k-norm; Sparse optimization; Support vector machine
File in questo prodotto:
File Dimensione Formato  
GGHU.pdf

non disponibili

Tipologia: versione post-print
Dimensione 337.47 kB
Formato Adobe PDF
337.47 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Gaudioso2020_Article_FeatureSelectionInSVMViaPolyhe.pdf

non disponibili

Descrizione: articolo principale
Tipologia: versione editoriale
Dimensione 347.57 kB
Formato Adobe PDF
347.57 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/287542
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 6
social impact