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Abstract: Support vector machines (SVMs), and other supervised learning techniques have been experimented for the bio-molecular diagnosis of malignancies, using also feature selection methods. The classification task is particularly difficult because of the high dimensionality and low cardinality of gene expression data. In this paper we investigate a different approach based on random subspace ensembles of SVMs: a set of base learners is trained and aggregated using subsets of features randomly drawn from the available DNA microarray data. Experimental results on the colon adenocarcinoma diagnosis and medulloblastoma clinical outcome prediction show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Expression-based classification of tumors requires stable, reliable and variance reduction methods, as DNA microarray data are characterized by low size, high dimensionality, noise and large biological variability. In order to address the variance and curse of dimensionality problems arising from this difficult task, we propose to apply bagged ensembles of support vector machines (SVM) and feature selection algorithms to the recognition of malignant tissues. Presented results show that bagged ensembles of SVMs are more reliable and achieve equal or better classification accuracy with respect to single SVMs, whereas feature selection methods can further enhance classification accuracy. [Copyright &y& Elsevier]