Publicação
Features Selection Algorithms for Classification of Voice Signals
| Resumo: | In 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. |
|---|---|
| Autores principais: | Silva, Letícia |
| Outros Autores: | Bispo, Bruno; Teixeira, João Paulo |
| Assunto: | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172976898605056 |
|---|---|
| author | Silva, Letícia |
| author2 | Bispo, Bruno Teixeira, João Paulo |
| author2_role | author author |
| author_facet | Silva, Letícia Bispo, Bruno Teixeira, João Paulo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Silva, Letícia\",\"Person.identifier.orcid\":\"0000-0003-3812-2794\"},{\"Person.name\":\"Bispo, Bruno\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Silva, Letícia Bispo, Bruno Teixeira, João Paulo |
| datacite.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-01-17T11:39:12Z |
| datacite.date.embargoed.fl_str_mv | 2022-01-17T11:39:12Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| datacite.titles.title.fl_str_mv | Features Selection Algorithms for Classification of Voice Signals |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Silva, Letícia Bispo, Bruno Teixeira, João Paulo |
| dc.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-01-17T11:39:12Z |
| dc.date.embargoed.fl_str_mv | 2022-01-17T11:39:12Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/24673 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| dc.title.fl_str_mv | Features Selection Algorithms for Classification of Voice Signals |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | In 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/4c9dd1c7-6318-4f94-bde3-d0b9fe398cc4/download |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 6817 - DCRRNI ID |
| id | ipb_7cf6afaec735dd12e9d292571bcd7739 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/24673 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/24673 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Silva, Letícia Silva, Letícia https://www.ciencia-id.pt/C01E-87BA-67D7 C01E-87BA-67D7 http://orcid.org/0000-0003-3812-2794 0000-0003-3812-2794 Bispo, Bruno Teixeira, João Paulo Teixeira, João Paulo https://www.ciencia-id.pt/4F15-B322-59B4 4F15-B322-59B4 http://orcid.org/0000-0002-6679-5702 0000-0002-6679-5702 |
| publishDate | 2021 |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engpt_PTIn 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.application/pdfpt_PTFeatures Selection Algorithms for Classification of Voice SignalsPersonalSilva, LetíciaDSpacehttp://dspace.org/items/a2aa1be8-574c-4e0b-afd6-d3c61efad820DSpacehttp://dspace.org/items/a2aa1be8-574c-4e0b-afd6-d3c61efad820SilvaLetíciaCiência IDhttps://www.ciencia-id.ptC01E-87BA-67D7ORCIDhttp://orcid.org0000-0003-3812-2794Bispo, BrunoPersonalTeixeira, João PauloDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfTeixeiraJoão PauloCiência IDhttps://www.ciencia-id.pt4F15-B322-59B4ORCIDhttp://orcid.org0000-0002-6679-5702Researcher IDhttps://www.researcherid.comN-6576-2013Scopus Author IDhttps://www.scopus.com57069567500HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISSNIsPartOf1877-0509DOIIsPartOf10.1016/j.procs.2021.01.2512022-01-17T11:39:12Z20212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/24673http://purl.org/coar/access_right/c_abf2open accessBackward eliminationForward selectionMultilinear regression analysisPearson correlationReliefFWelch's t-test639295 bytesFundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2021http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/4c9dd1c7-6318-4f94-bde3-d0b9fe398cc4/downloadProcedia Computer Science181948956 |
| spellingShingle | Features Selection Algorithms for Classification of Voice Signals Silva, Letícia Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| status | SINGLETON |
| subject.fl_str_mv | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| title | Features Selection Algorithms for Classification of Voice Signals |
| title_full | Features Selection Algorithms for Classification of Voice Signals |
| title_fullStr | Features Selection Algorithms for Classification of Voice Signals |
| title_full_unstemmed | Features Selection Algorithms for Classification of Voice Signals |
| title_short | Features Selection Algorithms for Classification of Voice Signals |
| title_sort | Features Selection Algorithms for Classification of Voice Signals |
| topic | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| topic_facet | Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test |
| url | http://hdl.handle.net/10198/24673 |
| visible | 1 |