A wrapper method for feature selection using Support Vector Machines

Maldonado S.; Weber R.

Abstract

We introduce a novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions. Our method is based on a sequential backward selection, using the number of errors in a validation subset as the measure to decide which feature to remove in each iteration. We compare our approach with other algorithms like a filter method or Recursive Feature Elimination SVM to demonstrate its effectiveness and efficiency. © 2009 Elsevier Inc. All rights reserved.

Más información

Título según WOS: A wrapper method for feature selection using Support Vector Machines
Título según SCOPUS: A wrapper method for feature selection using Support Vector Machines
Título de la Revista: INFORMATION SCIENCES
Volumen: 179
Número: 13
Editorial: Elsevier
Fecha de publicación: 2009
Página de inicio: 2208
Página final: 2217
Idioma: English
URL: http://linkinghub.elsevier.com/retrieve/pii/S0020025509000917
DOI:

10.1016/j.ins.2009.02.014

Notas: ISI, SCOPUS