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dc.contributor.authorHernández-Pereira, Elena
dc.contributor.authorÁlvarez-Estévez, Diego
dc.contributor.authorMoret-Bonillo, Vicente
dc.date.accessioned2017-02-10T18:42:03Z
dc.date.issued2015-10
dc.identifier.citationElena M. Hernández-Pereira, Diego Álvarez-Estévez, Vicente Moret-Bonillo, Automatic classification of respiratory patterns involving missing data imputation techniques, Biosystems Engineering 138 (2015), pp. 65–76es_ES
dc.identifier.issn1537-5110
dc.identifier.issn1537-5129
dc.identifier.urihttp://hdl.handle.net/2183/18101
dc.description.abstract[Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN 2013-40686-Pes_ES
dc.description.sponsorshipXunta de Galicia; GRC2014/35.es_ES
dc.language.isoenges_ES
dc.publisherAcademic Presses_ES
dc.relation.urihttp://dx.doi.org/10.1016/j.biosystemseng.2015.06.011es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectRespiratory pattern classificationes_ES
dc.subjectMissing data imputationes_ES
dc.subjectMachine learninges_ES
dc.titleAutomatic classification of respiratory patterns involving missing data imputation techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2017-10-31es_ES
dc.date.embargoLift2017-10-31
UDC.journalTitleBiosystems Engineeringes_ES
UDC.volume138es_ES
UDC.startPage65es_ES
UDC.endPage76es_ES
dc.identifier.doi10.1016/j.biosystemseng.2015.06.011


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