BOLSTERED ERROR ESTIMATOR WITH FEATURE SELECTION Conference Paper

abstract

• Classification and error estimation are fundamental problems in genomic applications which are typically characterized by large numbers of variables and small numbers of samples. A previously proposed bolstered error estimator was found to work well in the small-sample settings with modest numbers of features not requiring feature selection. In this simulation study, we have improved the method for estimation of the bolstering kernels, which leads to an improved bolstered error estimator that has significantly reduced root mean square error compared to widely-accepted cross-validation error estimator, and performed well over a range of models and model complexities.

name of conference

• 2009 IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Braga Neto, Ulisses
• Dougherty, Edward

published proceedings

• 2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009)

author list (cited authors)

• Sima, C., Vu, T., Braga-Neto, U. M., & Dougherty, E. R.

citation count

• 0

complete list of authors

• Sima, Chao||Vu, Thang||Braga-Neto, Ulisses M||Dougherty, Edward R

publication date

• January 2009

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.2009.5174343

International Standard Book Number (ISBN) 13

• 978-1-4244-4531-8

start page

• 17

end page

• +

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2009.5174343