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RMS bounds and sample size considerations for error estimation in linear discriminant analysis

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3 Author(s)
Zollanvari, A. ; Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA ; Braga-Neto, U.M. ; Dougherty, E.R.

The validity of a classifier depends on the precision of the error estimator used to estimate its true error. This paper considers the necessary sample size to achieve a given validity measure, namely RMS, for resubstitution and leave-one-out error estimators in the context of LDA. It provides bounds for the RMS between the true error and both the resubstitution and leave-one-out error estimators in terms of sample size and dimensionality. These bounds can be used to determine the minimum sample size in order to obtain a desired estimation accuracy, relative to RMS. To show how these results can be used in practice, a microarray classification problem is presented.

Published in:

Genomic Signal Processing and Statistics (GENSIPS), 2010 IEEE International Workshop on

Date of Conference:

10-12 Nov. 2010