Random subspace ensembles for the bio-molecular diagnosis of tumors

The bio-molecular diagnosis of malignancies, based on DNA microarray biotechnologies, is a difficult learning task, because of the high dimensionality and low cardinality of the data. Many supervised learning techniques, among them support vector machines (SVMs), have been experimented, using also feature selection methods to reduce the dimensionality of the data. In this paper we investigate an alternative approach based on random subspace ensemble methods. The high dimensionality of the data is reduced by randomly sampling subsets of features (gene expression levels), and accuracy is improved by aggregating the resulting base classifiers. Our experiments, in the area of the diagnosis of malignancies at bio-molecular level, show the effectiveness of the proposed approach.