A bi-objective feature selection algorithm for large omics datasets

Detalhes bibliográficos
Autor(a) principal: Cavique, Luís
Data de Publicação: 2018
Outros Autores: Mendes, Armando B., Martiniano, Hugo F. M. C., Correia, Luís
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.2/7648
Resumo: Special Issue: Fourth special issue on knowledge discovery and business intelligence.
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spelling A bi-objective feature selection algorithm for large omics datasetsFeature selectionLogical analysis of dataHeuristic decompositionBi-objective optimizationSpecial Issue: Fourth special issue on knowledge discovery and business intelligence.Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency based methods are a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the bi-objective version of the algorithm Logical Analysis of Inconsistent Data is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, heuristic decomposition uses parallel processing to solve a set covering problem and a cross-validation technique. The bi-objective solutions contain the number of reduced features and the accuracy. The algorithm is applied to omics datasets with genome-like characteristics of patients with rare diseases.The authors would like to thank the FCT support UID/Multi/04046/2013. This work used the EGI, European Grid Infrastructure, with the support of the IBERGRID, Iberian Grid Infrastructure, and INCD (Portugal).Wiley Online LibraryRepositório AbertoCavique, LuísMendes, Armando B.Martiniano, Hugo F. M. C.Correia, Luís2018-11-06T14:00:31Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.2/7648eng0266-4720https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12301info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T15:27:51Zoai:repositorioaberto.uab.pt:10400.2/7648Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:47:54.051846Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A bi-objective feature selection algorithm for large omics datasets
title A bi-objective feature selection algorithm for large omics datasets
spellingShingle A bi-objective feature selection algorithm for large omics datasets
Cavique, Luís
Feature selection
Logical analysis of data
Heuristic decomposition
Bi-objective optimization
title_short A bi-objective feature selection algorithm for large omics datasets
title_full A bi-objective feature selection algorithm for large omics datasets
title_fullStr A bi-objective feature selection algorithm for large omics datasets
title_full_unstemmed A bi-objective feature selection algorithm for large omics datasets
title_sort A bi-objective feature selection algorithm for large omics datasets
author Cavique, Luís
author_facet Cavique, Luís
Mendes, Armando B.
Martiniano, Hugo F. M. C.
Correia, Luís
author_role author
author2 Mendes, Armando B.
Martiniano, Hugo F. M. C.
Correia, Luís
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Aberto
dc.contributor.author.fl_str_mv Cavique, Luís
Mendes, Armando B.
Martiniano, Hugo F. M. C.
Correia, Luís
dc.subject.por.fl_str_mv Feature selection
Logical analysis of data
Heuristic decomposition
Bi-objective optimization
topic Feature selection
Logical analysis of data
Heuristic decomposition
Bi-objective optimization
description Special Issue: Fourth special issue on knowledge discovery and business intelligence.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-06T14:00:31Z
2018
2018-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.2/7648
url http://hdl.handle.net/10400.2/7648
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0266-4720
https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12301
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley Online Library
publisher.none.fl_str_mv Wiley Online Library
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