Variable selection and discrimination in gene expression data by genetic algorithms

Gene expression datasets usually have thousends of explanatory
variables which are observed on only few samples. Generally most
variables of a dataset have no effect and one is interested in
eliminating these irrelevant variables. In order to obtain a subset
of relevant variables an appropriate selection procedure is
necessary. In this paper we propose the selection of variables by
use of genetic algorithms with the logistic regression as
underlying modelling procedure. The selection procedure aims at
minimizing information criteria like AIC or BIC. It is demonstrated
that selection of variables by genetic algorithms yields models
which compete well with the best available classification
procedures in terms of test misclassification error.