Improving Parkinson's disease identification through evolutionary-based feature selection
Autor(a) principal: | |
---|---|
Data de Publicação: | 2011 |
Outros Autores: | , , , , |
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. |
id |
UNSP_f8ba61388467c0dcbcd3bba4e5b86065 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/73086 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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:29462024-08-05T13:58:15.763590Repositório 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_ |
1808128298336649216 |