Permutation tests for classification: Revisited

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

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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.

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Harvard

Ganz, M & Konukoglu, E 2017, Permutation tests for classification: Revisited. i 2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/PRNI.2017.7981495

APA

Ganz, M., & Konukoglu, E. (2017). Permutation tests for classification: Revisited. I 2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRNI.2017.7981495

Vancouver

Ganz M, Konukoglu E. Permutation tests for classification: Revisited. I 2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017. Institute of Electrical and Electronics Engineers Inc. 2017 https://doi.org/10.1109/PRNI.2017.7981495

Author

Ganz, Melanie ; Konukoglu, Ender. / Permutation tests for classification: Revisited. 2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017. Institute of Electrical and Electronics Engineers Inc., 2017.

Bibtex

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title = "Permutation tests for classification: Revisited",
abstract = "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.",
author = "Melanie Ganz and Ender Konukoglu",
year = "2017",
month = jul,
day = "14",
doi = "10.1109/PRNI.2017.7981495",
language = "English",
isbn = "9781538631591",
booktitle = "2017 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

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