Description

Abstract

          We introduce an embedded method that simultaneously selects relevant

features during classifier construction by penalizing each feature’s use in the dual

formulation of support vector machines (SVM). This approach called kernel-

penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel

eliminating features that have low relevance for the classifier. Additionally, KP-SVM

employs an explicit stopping condition, avoiding the elimination of features that

would negatively affect the classifier’s performance. We per-formed experiments

on four real-world benchmark problems comparing our approach with well-known

feature selection techniques. KP-SVM outperformed the alternative approaches

and determined consistently fewer relevant features.