Feature selection in the semivarying coefficient LS-SVR

Abstract

In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

Gene–Environment Interaction: A Variable Selection Perspective

“…Within a regression framework, boosting has strong connections to penalization ( [57−59]), which makes it a natural choice for detecting important G × E interactions ( [60,61]). Support vector machine, another popular machine learning technique which is tightly connected to penalization in the form of "hinge loss + ridge penalty", can also be adopted for G × E interactions ( [62,63]). Despite success in these studies, majority of the machine learning methods have been developed for epistasis studies ( [24,30,32,33]).…”