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Machine Learning methods have of late made significant efforts to solvingmultidisciplinary problems in the field of cancer classification usingmicroarray gene expression data. Feature subset selection methods can play animportant role in the modeling process, since these tasks are characterized bya large number of features and a few observations, making the modeling anon-trivial undertaking. In this particular scenario, it is extremely importantto select genes by taking into account the possible interactions with othergene subsets. This paper shows that, by accumulating the evidence in favour (oragainst) each gene along the search process, the obtained gene subsets mayconstitute better solutions, either in terms of predictive accuracy or genesize, or in both. The proposed technique is extremely simple and applicable ata negligible overhead in cost.

Exploiting the Accumulated Evidence for Gene Selection in Microarray Gene Expression Data