RMS BOUNDS AND SAMPLE SIZE CONSIDERATIONS FOR ERROR ESTIMATION IN LINEAR DISCRIMINANT ANALYSIS Conference Paper

abstract

• 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 microar-ray classification problem is presented. 2010 IEEE.

name of conference

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

authors

• Braga Neto, Ulisses
• Dougherty, Edward

published proceedings

• 2010 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS)

author list (cited authors)

• Zollanvari, A., Braga-Neto, U. M., & Dougherty, E. R.

citation count

• 0

complete list of authors

• Zollanvari, Amin||Braga-Neto, Ulisses M||Dougherty, Edward R

publication date

• January 2010

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics  Journal

Digital Object Identifier (DOI)

• 10.1109/gensips.2010.5719691

International Standard Book Number (ISBN) 13

• 9781612847924

start page

• 1

end page

• 4

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2010.5719691