Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

ABSTRACT:

Background

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process).

Results

With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: mode

SUBMITTER: Furlanello C 

PROVIDER: S-EPMC293475 | biostudies-literature | 2003 Nov

REPOSITORIES: biostudies-literature