PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bio-molecular cancer prediction with random subspace ensembles of Support Vector Machines
Alberto Bertoni, Raffaella Folgieri and Giorgio Valentini
Neurocomputing Volume 63, pp. 535-539, 2005.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2077
Deposited By:Giorgio Valentini
Deposited On:05 February 2006