  In this work a new way to calculate the multivariate joint entropy is
presented. This measure is the basis for a fast information-theoretic based
evaluation of gene relevance in a Microarray Gene Expression data context. Its
low complexity is based on the reuse of previous computations to calculate
current feature relevance. The mu-TAFS algorithm --named as such to
differentiate it from previous TAFS algorithms-- implements a simulated
annealing technique specially designed for feature subset selection. The
algorithm is applied to the maximization of gene subset relevance in several
public-domain microarray data sets. The experimental results show a notoriously
high classification performance and low size subsets formed by biologically
meaningful genes.
