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Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care

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datacite.subject.fosCiências Naturais: Ciências da Computação e da Informação
dc.contributor.authorGago, Pedro
dc.contributor.authorSantos, Manuel Filipe
dc.date.accessioned2025-05-15T15:57:50Z
dc.date.available2025-05-15T15:57:50Z
dc.date.issued2009
dc.description2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) held on 21-22 July, 2008 in Patras, Greece. Book series: Studies in Computational Intelligence
dc.description.abstractThe huge amount of data available in an Intensive Care Unit (ICU) makes ICUs an attractive field for data analysis. However, effective decision support systems operating in such an environment should not only be accurate but also as autonomous as possible, being capable of maintaining good performance levels without human intervention. Moreover, the complexity of an ICU setting is such that available data only manages to cover a limited part of the feature space. Such characteristics led us to investigate the development of ensemble update techniques capable of improving the discriminative power of the ensemble. Our chosen technique is inspired by the Dynamic Weighted Majority algorithm, an algorithm initially developed for the concept drift problem. In this paper we will show that in the problem we are addressing, simple weight updates do not improve results, whereas an ensemble, where we allow not only weight updates, but also the creation and eliminations of models, significantly increases classification performance.eng
dc.description.sponsorshipWe would like to thank the referees for their comments which helped improve this paper. Part of this work was supported by the Fundação para a Ciência e Tecnologia (grant SFRH BD 28840 2006). Financial support for this study was received from the FCT project PTDC/EIA/72819/2006 and from the Algoritmi research center.
dc.identifier.citationGago, P., Santos, M.F. (2009). Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care. In: Okun, O., Valentini, G. (eds) Applications of Supervised and Unsupervised Ensemble Methods. Studies in Computational Intelligence, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03999-7_14.
dc.identifier.doi10.1007/978-3-642-03999-7_14
dc.identifier.eissn1860-9503
dc.identifier.isbn9783642039980
dc.identifier.isbn9783642039997EISBN
dc.identifier.issn1860-949X
dc.identifier.urihttp://hdl.handle.net/10400.8/12892
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationINTCARE - Intelligent Decision Support System for Intensive Care
dc.relationSISTEMAS DE INFORMAÇÃO INTELIGENTES BASEADOS NA DESCOBERTA DE CONHECIMENTO EM BASES DE DADOS
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-642-03999-7_14
dc.relation.ispartofStudies in Computational Intelligence
dc.relation.ispartofApplications of Supervised and Unsupervised Ensemble Methods
dc.rights.uriN/A
dc.subjectclassifier ensemble
dc.subjectdata dimensionality
dc.titleEvaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Careeng
dc.typebook part
dspace.entity.typePublication
oaire.awardTitleINTCARE - Intelligent Decision Support System for Intensive Care
oaire.awardTitleSISTEMAS DE INFORMAÇÃO INTELIGENTES BASEADOS NA DESCOBERTA DE CONHECIMENTO EM BASES DE DADOS
oaire.awardURIhttp://hdl.handle.net/10400.8/12887
oaire.awardURIhttp://hdl.handle.net/10400.8/12888
oaire.citation.endPage265
oaire.citation.startPage251
oaire.citation.titleApplications of Supervised and Unsupervised Ensemble Methods
oaire.citation.volume245
oaire.fundingStream5876-PPCDTI
oaire.fundingStreamPIDDAC
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGago
person.givenNamePedro
person.identifier.orcid0000-0003-3404-9657
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relation.isAuthorOfPublication.latestForDiscoveryda8a69f8-5112-4415-b79b-bc9e2ed28131
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relation.isProjectOfPublication.latestForDiscovery13e85ba1-89fc-4101-9e99-d777a0b7225b

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The huge amount of data available in an Intensive Care Unit (ICU) makes ICUs an attractive field for data analysis. However, effective decision support systems operating in such an environment should not only be accurate but also as autonomous as possible, being capable of maintaining good performance levels without human intervention. Moreover, the complexity of an ICU setting is such that available data only manages to cover a limited part of the feature space. Such characteristics led us to investigate the development of ensemble update techniques capable of improving the discriminative power of the ensemble. Our chosen technique is inspired by the Dynamic Weighted Majority algorithm, an algorithm initially developed for the concept drift problem. In this paper we will show that in the problem we are addressing, simple weight updates do not improve results, whereas an ensemble, where we allow not only weight updates, but also the creation and eliminations of models, significantly increases classification performance.
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