Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.

ABSTRACT:

Background

Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tunin

SUBMITTER: Becker N 

PROVIDER: S-EPMC3113938 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature