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

Julio López, Sebastián Maldonado, Miguel Carrasco

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

19 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 estadounidense
Páginas (desde-hasta)377-389
Número de páginas13
PublicaciónInformation Sciences
Volumen429
DOI
EstadoPublicada - 1 mar 2018

Palabras clave

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

Huella Profundice en los temas de investigación de 'Double regularization methods for robust feature selection and SVM classification via DC programming'. En conjunto forman una huella única.

Citar esto