Double regularization methods for robust feature selection and SVM classification via DC programming

Julio López, Sebastián Maldonado*, Miguel Carrasco

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

36 Citas (Scopus)

Resumen

In this work, two novel formulations for embedded feature selection are presented. A second-order cone programming approach for Support Vector Machines is extended by adding a second regularizer to encourage feature elimination. The one- and the zero-norm penalties are used in combination with the Tikhonov regularization under a robust setting designed to correctly classify instances, up to a predefined error rate, even for the worst data distribution. The use of the zero norm leads to a nonconvex formulation, which is solved by using Difference of Convex (DC) functions, extending DC programming to second-order cones. Experiments on high-dimensional microarray datasets were performed, and the best performance was obtained with our approaches compared with well-known feature selection methods for Support Vector Machines.
Idioma originalInglés
Páginas (desde-hasta)377-389
Número de páginas13
PublicaciónInformation Sciences
Volumen429
DOI
EstadoPublicada - mar. 2018

Nota bibliográfica

Publisher Copyright:
© 2017 Elsevier Inc.

Palabras clave

  • Dc algorithm
  • Second-order cone programming
  • Support vector machines
  • Zero norm

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