  Machine Learning methods have of late made significant efforts to solving
multidisciplinary problems in the field of cancer classification using
microarray gene expression data. Feature subset selection methods can play an
important role in the modeling process, since these tasks are characterized by
a large number of features and a few observations, making the modeling a
non-trivial undertaking. In this particular scenario, it is extremely important
to select genes by taking into account the possible interactions with other
gene subsets. This paper shows that, by accumulating the evidence in favour (or
against) each gene along the search process, the obtained gene subsets may
constitute better solutions, either in terms of predictive accuracy or gene
size, or in both. The proposed technique is extremely simple and applicable at
a negligible overhead in cost.
