On the effectiveness of discretization on gene selection of microarray data

DNA microarray data is a challenging issue for machine learning researchers due to the high number of gene expression contained and the small samples sizes. To deal with this problem, feature selection methods, such as filters and wrappers, are typically applied to reduce the dimensionality. In this work, we apply a filter method before the classification and include a discretization step. The results obtained over ten different microarray data sets confirm the adequacy of the proposed method, that achieves better performances than the classifier alone. Besides, the combination method is also compared with the approaches of other authors (using wrappers and filters), outperforming the prediction accuracy and maintaining or even decreasing the number of genes required.