Improving Parkinson's disease identification through evolutionary-based feature selection

Detalhes bibliográficos
Autor(a) principal: Spadoto, André A.
Data de Publicação: 2011
Outros Autores: Guido, Rodrigo C., Carnevali, Felipe L., Pagnin, Andre F. [UNESP], Falcão, Alexandre X., Papa, João Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IEMBS.2011.6091936
http://hdl.handle.net/11449/73086
Resumo: Parkinson'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.
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spelling Improving Parkinson's disease identification through evolutionary-based feature selectionAutomatic identificationParkinson's diseasePossible solutionsTraining phaseAutomationNeurodegenerative diseasesFeature extractionParkinson'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.Institute of Physics at São Carlos University of São Paulo, São CarlosDepartment of Computing Federal University of São Carlos, São CarlosInstitute of Computing University of Campinas, CampinasDepartment of Computing Universidade Estadual Paulista (UNESP), BauruDepartment of Computing Universidade Estadual Paulista (UNESP), BauruUniversidade de São Paulo (USP)Universidade Federal de São Carlos (UFSCar)Universidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Spadoto, André A.Guido, Rodrigo C.Carnevali, Felipe L.Pagnin, Andre F. [UNESP]Falcão, Alexandre X.Papa, João Paulo [UNESP]2014-05-27T11:26:20Z2014-05-27T11:26:20Z2011-12-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject7857-7860http://dx.doi.org/10.1109/IEMBS.2011.6091936Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 7857-7860.1557-170Xhttp://hdl.handle.net/11449/7308610.1109/IEMBS.2011.60919362-s2.0-84055219309903918293274719465420862268080670000-0002-0924-8024Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSinfo:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/73086Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Improving Parkinson's disease identification through evolutionary-based feature selection
title Improving Parkinson's disease identification through evolutionary-based feature selection
spellingShingle Improving Parkinson's disease identification through evolutionary-based feature selection
Spadoto, André A.
Automatic identification
Parkinson's disease
Possible solutions
Training phase
Automation
Neurodegenerative diseases
Feature extraction
title_short Improving Parkinson's disease identification through evolutionary-based feature selection
title_full Improving Parkinson's disease identification through evolutionary-based feature selection
title_fullStr Improving Parkinson's disease identification through evolutionary-based feature selection
title_full_unstemmed Improving Parkinson's disease identification through evolutionary-based feature selection
title_sort Improving Parkinson's disease identification through evolutionary-based feature selection
author Spadoto, André A.
author_facet Spadoto, André A.
Guido, Rodrigo C.
Carnevali, Felipe L.
Pagnin, Andre F. [UNESP]
Falcão, Alexandre X.
Papa, João Paulo [UNESP]
author_role author
author2 Guido, Rodrigo C.
Carnevali, Felipe L.
Pagnin, Andre F. [UNESP]
Falcão, Alexandre X.
Papa, João Paulo [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Federal de São Carlos (UFSCar)
Universidade Estadual de Campinas (UNICAMP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Spadoto, André A.
Guido, Rodrigo C.
Carnevali, Felipe L.
Pagnin, Andre F. [UNESP]
Falcão, Alexandre X.
Papa, João Paulo [UNESP]
dc.subject.por.fl_str_mv Automatic identification
Parkinson's disease
Possible solutions
Training phase
Automation
Neurodegenerative diseases
Feature extraction
topic Automatic identification
Parkinson's disease
Possible solutions
Training phase
Automation
Neurodegenerative diseases
Feature extraction
description Parkinson'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.
publishDate 2011
dc.date.none.fl_str_mv 2011-12-26
2014-05-27T11:26:20Z
2014-05-27T11:26:20Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IEMBS.2011.6091936
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 7857-7860.
1557-170X
http://hdl.handle.net/11449/73086
10.1109/IEMBS.2011.6091936
2-s2.0-84055219309
9039182932747194
6542086226808067
0000-0002-0924-8024
url http://dx.doi.org/10.1109/IEMBS.2011.6091936
http://hdl.handle.net/11449/73086
identifier_str_mv Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, p. 7857-7860.
1557-170X
10.1109/IEMBS.2011.6091936
2-s2.0-84055219309
9039182932747194
6542086226808067
0000-0002-0924-8024
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 7857-7860
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799964427417550848

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