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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/73086
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dc.contributor.authorSpadoto, André A.-
dc.contributor.authorGuido, Rodrigo C.-
dc.contributor.authorCarnevali, Felipe L.-
dc.contributor.authorPagnin, Andre F.-
dc.contributor.authorFalcão, Alexandre X.-
dc.contributor.authorPapa, João Paulo-
dc.date.accessioned2014-05-27T11:26:20Z-
dc.date.accessioned2016-10-25T18:36:22Z-
dc.date.available2014-05-27T11:26:20Z-
dc.date.available2016-10-25T18:36:22Z-
dc.date.issued2011-12-26-
dc.identifierhttp://dx.doi.org/10.1109/IEMBS.2011.6091936-
dc.identifier.citationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 7857-7860.-
dc.identifier.issn1557-170X-
dc.identifier.urihttp://hdl.handle.net/11449/73086-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/73086-
dc.description.abstractParkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.en
dc.format.extent7857-7860-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectAutomatic identification-
dc.subjectParkinson's disease-
dc.subjectPossible solutions-
dc.subjectTraining phase-
dc.subjectAutomation-
dc.subjectNeurodegenerative diseases-
dc.subjectFeature extraction-
dc.titleImproving Parkinson's disease identification through evolutionary-based feature selectionen
dc.typeoutro-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationInstitute of Physics at São Carlos University of São Paulo, São Carlos-
dc.description.affiliationDepartment of Computing Federal University of São Carlos, São Carlos-
dc.description.affiliationInstitute of Computing University of Campinas, Campinas-
dc.description.affiliationDepartment of Computing Universidade Estadual Paulista (UNESP), Bauru-
dc.description.affiliationUnespDepartment of Computing Universidade Estadual Paulista (UNESP), Bauru-
dc.identifier.doi10.1109/IEMBS.2011.6091936-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS-
dc.identifier.scopus2-s2.0-84055219309-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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