Feature selection in the classification of high-dimension data Conference Paper

abstract

• Contemporary biological technologies produce extremely high-dimensional data sets with limited samples which demands feature selection in classifier design. Heretofore, dimensionalities considered in the existing comparative studies for feature selection are nowhere near those currently being used. This study compares some basic feature-selection methods in settings of 20,000 features, where it defines distribution models involving different kinds of relations among the features. The study evaluates the performances of different feature selection algorithms, which show some general trends relative to sample size and relations among the features. 2008 IEEE.

name of conference

• 2008 IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Dougherty, Edward

published proceedings

• 2008 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS

author list (cited authors)

• Hua, J., Tembe, W., & Dougherty, E. R.

citation count

• 14

complete list of authors

• Hua, Jianping||Tembe, Waibhav||Dougherty, Edward R

publication date

• June 2008

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

• Bioengineering
• Generic Health Relevance

Digital Object Identifier (DOI)

• 10.1109/gensips.2008.4555665

International Standard Book Number (ISBN) 13

• 978-1-4244-2371-2

start page

• 39

end page

• +

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2008.4555665