<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>In order to study the molecular biological differences between normal and diseased tissues,
it is desirable to perform classification among diseases and stages of disease using
microarray-based gene-expression values. Owing to the limited number of microarrays
typically used in these studies, serious issues arise with respect to the design, performance
and analysis of classifiers based on microarray data. This paper reviews some fundamental
issues facing small-sample classification: classification rules, constrained classifiers, error
estimation and feature selection. It discusses both unconstrained and constrained classifier
design from sample data, and the contributions to classifier error from constrained
optimization and lack of optimality owing to design from sample data. The difficulty with
estimating classifier error when confined to small samples is addressed, particularly
estimating the error from training data. The impact of small samples on the ability to
include more than a few variables as classifier features is explained.</p>
[]
Comp Funct Genomics. 2001 Feb; 2(1):28-34