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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Golland, P., Liang, F., Mukherjee, S., Panchenko, D. (2005). Permutation Tests for Classification. In: Auer, P., Meir, R. (eds) Learning Theory. COLT 2005. Lecture Notes in Computer Science(), vol 3559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11503415_34
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DOI: https://doi.org/10.1007/11503415_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26556-6
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