Affiliations: [a] Facultad de Ingeniería y Ciencias Aplicadas,
Universidad de los Andes, Santiago, Chile | [b] Facultad de Ingeniería, Universidad Diego Portales,
Ejército, Santiago, Chile
Correspondence:
[*]
Corresponding author: Sebastián Maldonado,
Facultad de Ingeniería y Ciencias Aplicadas,
Universidad de los Andes, Mons, Álvaro del Portillo 12455, Las Condes,
Santiago, Chile. E-mail:[email protected]
Abstract: Feature selection is an important machine learning topic, especially in high
dimensional applications, such as cancer prediction with microarray data.
This work addresses the issue of high dimensionality of feature selection for linear and
kernel-based Support Vector Machines (SVMs) considering second-order cone programming
formulations. These formulations provide a robust and efficient
framework for classification, while an adequate feature selection
process avoids errors in the estimation of means and covariances. Our
approach is based on a sequential backward elimination which uses
different linear and kernel-based contribution measures to determine the feature relevance.
Experimental results with microarray datasets demonstrate
the effectiveness in terms of predictive performance and construction of a low-dimensional data representation.
Keywords: Second-order cone programming, Support Vector Machines, feature selection, kernel methods, data mining
