Sign-consistency based variable importance for machine learning in brain imaging Articles uri icon

publication date

  • March 2019

start page

  • 593

end page

  • 609

issue

  • 4

volume

  • 17

International Standard Serial Number (ISSN)

  • 1539-2791

Electronic International Standard Serial Number (EISSN)

  • 1559-0089

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 is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.

keywords

  • alzheimer disease; bagging; mri; schizophrenia; support vector machines; variable importance