Permutation tests for classification: Revisited

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Melanie Ganz, Ender Konukoglu

Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a non-parametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function to label new examples into one of two groups). Our approach adopts permutation tests, first developed in classical statistics for hypothesis testing, to estimate how likely we are to obtain the observed classification performance, as measured by testing on a hold-out set or cross-validation, by chance. We demonstrate the method on examples of both structural and functional neuroimaging studies.
Original languageEnglish
Title of host publication2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date14 Jul 2017
ISBN (Print)9781538631591
DOIs
Publication statusPublished - 14 Jul 2017
Series2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017

ID: 214644204