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

机译：基于标志一致性的机器学习变量重要性脑成像

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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.
机译：阻碍脑成像使用监督分类算法的重要问题是，每单个主题的变量数远远超过培训科目的数量。导出多元性的变量重要性测量在这种情况下成为挑战。本文提出了一种新的可变重要性称为签名 - 一致性袋（SCB）的衡量标准。 SCB通过分析线性支持向量机（SVM）分类器的集合中的相应权重的标志一致性来捕获变量重要性。此外，通过转导的共形分析增强了SCB变形重点。当数据可以假设是异质的，这个额外的步骤很重要。最后，完成了这些SCB可变重要性措施的提议，通过参数假设试验的变量重要性进行了衍生。将新的重要措施与基于T-Test的单变量和基于SVM的多变量的多变量变量进行了比较，使用解剖学和功能磁共振成像数据。所获得的结果表明，在可重复性和分类准确性方面，基于SCB的重要性措施优于比较的方法。

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