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Features Selection Algorithms for Classification of Voice Signals

2021Conference paper Open access
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dc.contributor.authorSilva, Letícia
dc.contributor.authorBispo, Bruno
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2022-01-17T11:39:12Z
dc.date.available2022-01-17T11:39:12Z
dc.date.issued2021
dc.description.abstractIn data mining problems, the high dimensionality of the input features can affect the performance of the process. In this way, the features selection methods appear as a solution to the problems encountered when analyzing databases with large dimensions. This article presents the implementation of the Pearson's linear correlation, ReliefF, Welch's t-test and multilinear regression based algorithms with forwards selection and backward elimination direction for the selection of acoustic features for the task of voice pathologies identification. The best set of selected features improved the accuracy and F1-score from 83% to 92% (9 points of percentage), using the ReliefF algorithm.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, Letícia; Bispo, Bruno; Teixeira, João Paulo (2021). Features selection algorithms for classification of voice signals. In International Conference on ENTERprise Information Systems (CENTERIS), International Conference on Project MANagement (ProjMAN), International Conference on Health and Social Care Information Systems and Technologies (HCist). Procedia Computer Science. ISSN 1877-0509. p. 948-956.pt_PT
dc.identifier.doi10.1016/j.procs.2021.01.251pt_PT
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/10198/24673
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBackward eliminationpt_PT
dc.subjectForward selectionpt_PT
dc.subjectMultilinear regression analysispt_PT
dc.subjectPearson correlationpt_PT
dc.subjectReliefFpt_PT
dc.subjectWelch's t-testpt_PT
dc.titleFeatures Selection Algorithms for Classification of Voice Signalspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage956pt_PT
oaire.citation.startPage948pt_PT
oaire.citation.titleProcedia Computer Sciencept_PT
oaire.citation.volume181pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva
person.familyNameTeixeira
person.givenNameLetícia
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-idC01E-87BA-67D7
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0003-3812-2794
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
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