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

Background: Classification and variable selection play an important role in knowledge discovery in highdimensional 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 tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results: Feature selection methods with combine...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Natalia Becker, Grischa Toedt, Peter Lichter, Axel Benner
• Keywords: Biometrics, BMCBI 2011, Interval Search, Support Vector Machine, Svm Models