ML Anthology

Permutation Tests for Classification

Polina Golland, Feng Liang, Sayan Mukherjee, Dmitry Panchenko
COLT 2005 pp. 501-515
doi:10.1007/11503415_34 /colt/2005/golland2005colt-permutation/

Abstract

We describe a permutation procedure used extensively in classification problems in computational biology and medical imaging. We empirically study the procedure on simulated data and real examples from neuroimaging studies and DNA microarray analysis. A theoretical analysis is also suggested to assess the asymptotic behavior of the test. An interesting observation is that concentration of the permutation procedure is controlled by a Rademacher average which also controls the concentration of empirical errors to expected errors.

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Text

Golland et al. "Permutation Tests for Classification." Annual Conference on Computational Learning Theory, 2005. doi:10.1007/11503415_34

Markdown

[Golland et al. "Permutation Tests for Classification." Annual Conference on Computational Learning Theory, 2005.](https://mlanthology.org/colt/2005/golland2005colt-permutation/) doi:10.1007/11503415_34

BibTeX

@inproceedings{golland2005colt-permutation,
  title     = {{Permutation Tests for Classification}},
  author    = {Golland, Polina and Liang, Feng and Mukherjee, Sayan and Panchenko, Dmitry},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2005},
  pages     = {501-515},
  doi       = {10.1007/11503415_34},
  url       = {https://mlanthology.org/colt/2005/golland2005colt-permutation/}
}