What can we expect from high-dimensional feature selection Conference Paper

abstract

• High-throughput technologies for rapid measurement of vast numbers of biological variables like cDNA microarray technology offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Through a regression approach, we found that (1) it is unlikely that feature selection will yield a feature set whose error is close to that of the optimal feature set; and (2) the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist. 2006 IEEE.

name of conference

• 2006 IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Dougherty, Edward

published proceedings

• 2006 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS

author list (cited authors)

• Sima, C., & Dougherty, E. R.

citation count

• 0

complete list of authors

• Sima, Chao||Dougherty, Edward R

publication date

• May 2006

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

keywords

• 31 Biological Sciences
• 3102 Bioinformatics And Computational Biology
• Bioengineering

Digital Object Identifier (DOI)

• 10.1109/GENSIPS.2006.353171

International Standard Book Number (ISBN) 10

• 1424403855

International Standard Book Number (ISBN) 13

• 978-1-4244-0384-4

start page

• 91

end page

• +

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2006.353171