Bio-molecular cancer prediction with random subspace ensembles of Support Vector Machines
Alberto Bertoni, Raffaella Folgieri and Giorgio Valentini
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.