Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging.

ABSTRACT: An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures

SUBMITTER: Gomez-Verdejo V 

PROVIDER: S-EPMC6841656 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature