Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/92963
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dc.creatorRaúl Ramos-Pollán
dc.creatorMiguel Ángel Guevara-López
dc.creatorEugénio Oliveira
dc.date.accessioned2019-02-03T04:41:49Z-
dc.date.available2019-02-03T04:41:49Z-
dc.date.issued2012
dc.identifier.issn0148-5598
dc.identifier.othersigarra:58502
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/92963-
dc.description.abstractThis paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.
dc.language.isoeng
dc.rightsrestrictedAccess
dc.subjectInformática, Ciências da saúde
dc.subjectInformatics, Health sciences
dc.titleA Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources
dc.typeArtigo em Revista Científica Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.1007/s10916-011-9692-3
dc.identifier.authenticusP-002-7JJ
dc.subject.fosCiências médicas e da saúde::Ciências da saúde
dc.subject.fosMedical and Health sciences::Health sciences
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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