Permutation tests for classification: Revisited
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Permutation tests for classification: Revisited. / Ganz, Melanie; Konukoglu, Ender.
2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017. Institute of Electrical and Electronics Engineers Inc., 2017.Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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TY - CHAP
T1 - Permutation tests for classification: Revisited
AU - Ganz, Melanie
AU - Konukoglu, Ender
PY - 2017/7/14
Y1 - 2017/7/14
N2 - 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.
AB - 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.
UR - http://www.mendeley.com/research/permutation-tests-classification-revisited
U2 - 10.1109/PRNI.2017.7981495
DO - 10.1109/PRNI.2017.7981495
M3 - Book chapter
SN - 9781538631591
BT - 2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -
ID: 214644204