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dc.contributor.authorElyan, Eyad
dc.contributor.authorGaber, Mohamed Medhat
dc.date.accessioned2016-09-09T15:37:07Z
dc.date.available2016-09-09T15:37:07Z
dc.date.issued2015-09-22en
dc.identifier.citationELYAN, E. and GABER, M.M. 2015. A fine-grained random forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], FirstOnline. Available from: http://dx.doi.org/10.1007/s00521-015-2064-zen
dc.identifier.issn0941-0643en
dc.identifier.issn1433-3058en
dc.identifier.urihttp://hdl.handle.net/10059/1644
dc.description.abstractClass decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable data set without the need for feature engineering required by techniques like support vector machines and deep learning. For ensembles, the decomposition is a natural way to increase diversity, a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of data sets that are mainly related to the medical domain. Results reported in this paper show clearly that our method has significantly improved the accuracy of Random Forests.en
dc.language.isoengen
dc.publisherSpringeren
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMachine learningen
dc.subjectRandom Forestsen
dc.subjectClusteringen
dc.subjectEnsemble learningen
dc.titleA fine-grained random forests using class decomposition: an application to medical diagnosis.en
dc.typeJournal articlesen
dc.publisher.urihttp://dx.doi.org/10.1007/s00521-015-2064-zen
dcterms.dateAccepted2015-09-08en
dcterms.publicationdate2016-11-01
refterms.accessExceptionNAen
refterms.dateDeposit2016-09-09en
refterms.dateEmbargoEnd2016-09-22en
refterms.dateFCA2016-09-22en
refterms.dateFCD2016-09-09en
refterms.dateFreeToDownload2016-09-22en
refterms.dateFreeToRead2016-09-22en
refterms.dateToSearch2016-09-22en
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2015-09-22en
rioxxterms.typeJournal Article/Reviewen
rioxxterms.versionAMen


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