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In this work a new way to calculate the multivariate joint entropy ispresented. This measure is the basis for a fast information-theoretic basedevaluation of gene relevance in a Microarray Gene Expression data context. Itslow complexity is based on the reuse of previous computations to calculatecurrent feature relevance. The mu-TAFS algorithm --named as such todifferentiate it from previous TAFS algorithms-- implements a simulatedannealing technique specially designed for feature subset selection. Thealgorithm is applied to the maximization of gene subset relevance in severalpublic-domain microarray data sets. The experimental results show a notoriouslyhigh classification performance and low size subsets formed by biologicallymeaningful genes.

Feature Selection for Microarray Gene Expression Data using Simulated Annealing guided by the Multivariate Joint Entropy