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 for 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. Preprint submitted to ElsevierApril 5, 2017. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/124453 doi: bioRxiv preprint first posted online 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.

Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities

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Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging

“…To address these shortcomings, we propose a Regularized Bagged -Cannonical Correlation Analysis (RB-CCA) method that is inspired by a recently proposed parsi-monious Multivariate Analysis (pMVA) method for FE and FS (Muñoz-Romero et al 2017). However, unlike with pMVA, the FS procedure of the RB-CCA is implemented by the calculation of a feature-wise sign consistency, analysed in Gomez-Verdejo et al (2019), through a bagged Cannonical Correlation Analysis (CCA) approach. This, combined with a statistical test introduced in this paper assigning a p-value for the relevance of each feature, comprises an automatic feature selection method of the optimum characteristics for neuroimaging problems.…”