An ensemble approach to improved prediction from multitype data

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dc.creatorClarke, Jennifer
dc.creatorSeo, David
dc.date2008-05-21
dc.date.accessioned2026-07-07T12:19:10Z
dc.date.available2026-07-07T12:19:10Z
dc.descriptionWe have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based on logic trees. The results of these models are then compared to the results of a model based on existing data with the objective of combining model results to achieve the most accurate predictions. The combination of model predictions is aided by the use of support vector machines to identify subspaces of the covariate space in which specific models lead to successful predictions. We demonstrate our approach in the analysis of single nucleotide polymorphism (SNP) data and traditional clinical risk factors for the prediction of coronary heart disease.
dc.descriptionPublished in at http://dx.doi.org/10.1214/074921708000000219 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0805.3286
dc.identifierhttp://arxiv.org/abs/0805.3286
dc.identifierIMS Collections 2008, Vol. 3, 302-317
dc.identifierdoi:10.1214/074921708000000219
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212657
dc.subjectApplications
dc.subjectMethodology
dc.subjectMachine Learning
dc.subject62M20, 62H30 (Primary) 62P10 (Secondary)
dc.titleAn ensemble approach to improved prediction from multitype data
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