The microarray report measures the expressions of tens of thousands of genes, producing a feature vector that is high in dimensionality and that contains much irrelevant information. This dimensionality degrades classification performance. Moreover, datasets typically contain few samples for training, leading to the curse of dimensionality problem. It is essential, therefore, to find good methods for reducing the size of the feature set.