Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis

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dc.contributor.author Devos, Andy
dc.contributor.author Van Huffel, Sabine
dc.contributor.author Lu, Chuan
dc.contributor.author Suykens, Johan A. K.
dc.contributor.author Arus, Carles
dc.date.accessioned 2010-01-15T00:19:53Z
dc.date.available 2010-01-15T00:19:53Z
dc.date.issued 2007-05
dc.identifier.citation Devos , A , Van Huffel , S , Lu , C , Suykens , J A K & Arus , C 2007 , ' Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis ' IEEE Transactions on Information Technology in BioMedicine , vol 11 , no. 3 , pp. 338-347 . , 10.1109/TITB.2006.889702 en
dc.identifier.other PURE: 143397
dc.identifier.other dspace: 2160/3984
dc.identifier.uri http://hdl.handle.net/2160/3984
dc.identifier.uri http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=4167897&isnumber=4167888 en
dc.description C. Lu, A. Devos, J.A.K. Suykens, C. Arus, S. Van Huffel, Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis, IEEE Transactions on Information Technology in Biomedicine, May 2007, Volume 11, Issue 3, Pp: 338-347. Sponsorship: This work was supported by the projects of IUAP Phase V-22, of the KUL MEFISTO-666 and IDO/99/03, the FWO projects G.0407.02 and G.0269.02, and EU projects BIOPATTERN (FP6-2002-IST 508803), eTUMOUR(FP6-2002-LIFESCIHEALTH 503094) and HEALTHagents (FP6-2005-IST 027214). CL was supported by a doctoral grant of K.U.Leuven and the BBSRC MetRO project with Aberystwyth University. en
dc.description.abstract This work investigates variable selection and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic variable selection algorithm. Selected variables are fed to the kernel based probabilistic classifiers: Bayesian least squares support vector machines (LS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both variable selection and model building in order to improve the reliability of the selected variables and the predictive performance. This modelling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other variable selection methods. It is shown that the use of bagging can improve the reliability and stability of both variable selection and model prediction. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof IEEE Transactions on Information Technology in BioMedicine en
dc.title Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1109/TITB.2006.889702
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en


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