Feature-selection overfitting with small-sample classifier design Academic Article

abstract

• The role of feature selection in overfitting the data and how this is exacerbated by high dimensionality and small samples is described. The best feature must be found from among all possible feature subsets. Multivariate prediction, and error estimation can severely impact feature selection while redundancy. A feature-selection algorithm is part of the classification rule when used. If a feature-selection algorithm reduces the number of variables to m

authors

• Dougherty, Edward

published proceedings

• IEEE INTELLIGENT SYSTEMS

altmetric score

• 3

author list (cited authors)

• Dougherty, E. R.

citation count

• 141

complete list of authors

• Dougherty, ER

publication date

• November 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Intelligent Systems  Journal

keywords

• Bioengineering

Digital Object Identifier (DOI)

• 10.1109/mis.2005.105

start page

• 64

end page

• 66

volume

• 20

issue

• 6

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fmis.2005.105