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.

中文翻译：

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脑成像中基于符号一致性的机器学习的变量重要性。

阻碍在脑成像中使用监督分类算法的一个重要问题是，每个单个对象的变量数量远远超过可用训练对象的数量。在这种情况下，得出具有可变重要性的多元度量成为一个挑战。本文提出了一种新的衡量变量重要性的方法，称为符号一致性装袋（SCB）。SCB通过分析一组线性支持向量机（SVM）分类器中相应权重的符号一致性来捕获变量的重要性。此外，通过转导保形分析提高了SCB变量的重要性。当可以认为数据是异构的时，这一额外的步骤很重要。最后，这些SCB变量重要性度量的建议是通过变量重要性的参数假设检验得出的。使用解剖和功能性磁共振成像数据，将新的重要性指标与基于t检验的单变量和基于SVM的多元变量重要性进行了比较。获得的结果表明，基于新的基于SCB的重要性度量在可重复性和分类准确性方面优于所比较的方法。