Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/9140 |
Resumo: | The procedure used to select a subset of suitable features in a given data set consists in variable selection, which is important when the dataset contains large number of variables and many of them are redundant. Multivariate calibration combines variable selection with statistical techniques to build mathematical models which relate the data to a given property of interest in order to predict this property by selecting informative variables. In this context, variable selection techniques have been widely applied to the solution of several optimization problems. For instance, Genetic Algorithms (GAs) are easy to implement and consist in a population-based model that uses selection and recombination operators to generate new solutions. However, usually in multivariate calibration the dataset present a considerable correlation degree among variables and this provides an evidence about the problem not being properly decomposed. Moreover, some studies in literature have claimed genetic operators used by GAs can cause the building blocks (BBs) disruption of viable solutions. Therefore, this work aims to claim that selecting variables in multivariate calibration is a non-completely decomposable problem (hypothesis 1) as well as that recombination operators affects the non-decomposability assumption (hypothesis 2). Additionally, we are proposing two heuristics, one local search-based operator and two versions of an Epistasis-based Feature Selection Algorithm (EbFSA) to improve model prediction performance and avoid BBs disruption. Based on the performed inquiry and experimental results, we are able to endorse the viability of our hypotheses and demonstrate EbFSA can overcome some traditional algorithms. |
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Soares, Anderson da Silvahttp://lattes.cnpq.br/1096941114079527Coelho, Clarimar Joséhttp://lattes.cnpq.br/1350166605717268Soares, Anderson da SilvaCoelho, Clarimar JoséCamilo Junior, Celso GonçalvesSoares, Fabrízzio Alphonsus Alves de Melo NunesOliveira, Anselmo Elcana dehttp://lattes.cnpq.br/4467252868250571Paula, Lauro Cássio Martins de2018-12-12T10:36:08Z2018-12-06PAULA, Lauro Cássio Martins de. Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis. 2018. 116 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2018.http://repositorio.bc.ufg.br/tede/handle/tede/9140The procedure used to select a subset of suitable features in a given data set consists in variable selection, which is important when the dataset contains large number of variables and many of them are redundant. Multivariate calibration combines variable selection with statistical techniques to build mathematical models which relate the data to a given property of interest in order to predict this property by selecting informative variables. In this context, variable selection techniques have been widely applied to the solution of several optimization problems. For instance, Genetic Algorithms (GAs) are easy to implement and consist in a population-based model that uses selection and recombination operators to generate new solutions. However, usually in multivariate calibration the dataset present a considerable correlation degree among variables and this provides an evidence about the problem not being properly decomposed. Moreover, some studies in literature have claimed genetic operators used by GAs can cause the building blocks (BBs) disruption of viable solutions. Therefore, this work aims to claim that selecting variables in multivariate calibration is a non-completely decomposable problem (hypothesis 1) as well as that recombination operators affects the non-decomposability assumption (hypothesis 2). Additionally, we are proposing two heuristics, one local search-based operator and two versions of an Epistasis-based Feature Selection Algorithm (EbFSA) to improve model prediction performance and avoid BBs disruption. Based on the performed inquiry and experimental results, we are able to endorse the viability of our hypotheses and demonstrate EbFSA can overcome some traditional algorithms.Seleção de variáveis é um procedimento para selecionar um subconjunto de características viáveis em um conjunto de dados, o qual se torna importante quando esse conjunto contém muitas variáveis redundantes. A calibração multivariada combina seleção de variáveis com técnicas estatísticas para construir modelos matemáticos com o intuito de predizer uma propriedade de interesse. Nesse contexto, técnicas de seleção têm sido aplicadas na solução de diversos problemas. Por exemplo, Algoritmos Genéticos (AGs) são fáceis de implementar e consistem em um modelo baseado em população, o qual utiliza operadores de seleção e recombinação para gerar novos indivíduos. No entanto, geralmente em calibração multivariada, o conjunto de dados apresenta um grau de correlação considerável entre as variáveis e isso nos fornece uma evidência de que tal problema não pode ser decomposto adequadamente. Além disso, alguns estudos da literatura têm afirmado que os operadores genéticos utilizados pelos AGs podem causar o rompimento dos Blocos Construtores (Building Blocks - BBs) das soluções viáveis. Portanto, este trabalho objetiva demonstrar que a seleção de variáveis em calibração multivariada é um problema não-completamente decomponível (hipótese 1), assim como que operadores de recombinação afetam a presunção de não-decomponibilidade (hipótese 2). Adicionalmente, este trabalho propõe duas heurísticas, um operador de busca local e duas versões de um Algoritmo para Seleção de Variáveis baseado em Epistasia (EbFSA) para aprimorar a capacidade de predição do modelo e evitar o rompimento de BBs. Baseando-se na pesquisa realizada e nos resultados obtidos, torna-se possível confirmar a viabilidade de nossas hipóteses e demonstrar que o EbFSA consegue superar alguns algoritmos tradicionais.Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-12-12T10:14:40Z No. of bitstreams: 2 Tese - Lauro Cássio Martins de Paula - 2018.pdf: 7484273 bytes, checksum: a3c47ef9c05d03a8dce4dce89a2df34b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-12-12T10:36:08Z (GMT) No. of bitstreams: 2 Tese - Lauro Cássio Martins de Paula - 2018.pdf: 7484273 bytes, checksum: a3c47ef9c05d03a8dce4dce89a2df34b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2018-12-12T10:36:08Z (GMT). No. of bitstreams: 2 Tese - Lauro Cássio Martins de Paula - 2018.pdf: 7484273 bytes, checksum: a3c47ef9c05d03a8dce4dce89a2df34b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-12-06Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfengUniversidade Federal de GoiásPrograma de Pós-graduação em Ciência da Computação em Rede UFG/UFMS (INF)UFGBrasilInstituto de Informática - INF (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessMultivariate calibrationVariable selectionGenetic algorithmBuilding blocksCalibração multivariadaSeleção de variáveisAlgoritmo genéticoBlocos construtoresCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOVariable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesisSeleção de variáveis em calibração multivariada considerando a presunção de não-decomponibilidade e a hipótese de blocos construtoresinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis7383127587728995171600600600600-771226673463364476836717112058112045092075167498588264571reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
dc.title.alternative.por.fl_str_mv |
Seleção de variáveis em calibração multivariada considerando a presunção de não-decomponibilidade e a hipótese de blocos construtores |
title |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
spellingShingle |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis Paula, Lauro Cássio Martins de Multivariate calibration Variable selection Genetic algorithm Building blocks Calibração multivariada Seleção de variáveis Algoritmo genético Blocos construtores CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
title_full |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
title_fullStr |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
title_full_unstemmed |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
title_sort |
Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis |
author |
Paula, Lauro Cássio Martins de |
author_facet |
Paula, Lauro Cássio Martins de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Soares, Anderson da Silva |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/1096941114079527 |
dc.contributor.advisor-co1.fl_str_mv |
Coelho, Clarimar José |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/1350166605717268 |
dc.contributor.referee1.fl_str_mv |
Soares, Anderson da Silva |
dc.contributor.referee2.fl_str_mv |
Coelho, Clarimar José |
dc.contributor.referee3.fl_str_mv |
Camilo Junior, Celso Gonçalves |
dc.contributor.referee4.fl_str_mv |
Soares, Fabrízzio Alphonsus Alves de Melo Nunes |
dc.contributor.referee5.fl_str_mv |
Oliveira, Anselmo Elcana de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4467252868250571 |
dc.contributor.author.fl_str_mv |
Paula, Lauro Cássio Martins de |
contributor_str_mv |
Soares, Anderson da Silva Coelho, Clarimar José Soares, Anderson da Silva Coelho, Clarimar José Camilo Junior, Celso Gonçalves Soares, Fabrízzio Alphonsus Alves de Melo Nunes Oliveira, Anselmo Elcana de |
dc.subject.eng.fl_str_mv |
Multivariate calibration Variable selection Genetic algorithm Building blocks |
topic |
Multivariate calibration Variable selection Genetic algorithm Building blocks Calibração multivariada Seleção de variáveis Algoritmo genético Blocos construtores CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.por.fl_str_mv |
Calibração multivariada Seleção de variáveis Algoritmo genético Blocos construtores |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
The procedure used to select a subset of suitable features in a given data set consists in variable selection, which is important when the dataset contains large number of variables and many of them are redundant. Multivariate calibration combines variable selection with statistical techniques to build mathematical models which relate the data to a given property of interest in order to predict this property by selecting informative variables. In this context, variable selection techniques have been widely applied to the solution of several optimization problems. For instance, Genetic Algorithms (GAs) are easy to implement and consist in a population-based model that uses selection and recombination operators to generate new solutions. However, usually in multivariate calibration the dataset present a considerable correlation degree among variables and this provides an evidence about the problem not being properly decomposed. Moreover, some studies in literature have claimed genetic operators used by GAs can cause the building blocks (BBs) disruption of viable solutions. Therefore, this work aims to claim that selecting variables in multivariate calibration is a non-completely decomposable problem (hypothesis 1) as well as that recombination operators affects the non-decomposability assumption (hypothesis 2). Additionally, we are proposing two heuristics, one local search-based operator and two versions of an Epistasis-based Feature Selection Algorithm (EbFSA) to improve model prediction performance and avoid BBs disruption. Based on the performed inquiry and experimental results, we are able to endorse the viability of our hypotheses and demonstrate EbFSA can overcome some traditional algorithms. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-12-12T10:36:08Z |
dc.date.issued.fl_str_mv |
2018-12-06 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PAULA, Lauro Cássio Martins de. Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis. 2018. 116 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2018. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/9140 |
identifier_str_mv |
PAULA, Lauro Cássio Martins de. Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis. 2018. 116 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2018. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/9140 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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7383127587728995171 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 |
dc.relation.department.fl_str_mv |
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dc.relation.cnpq.fl_str_mv |
3671711205811204509 |
dc.relation.sponsorship.fl_str_mv |
2075167498588264571 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
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application/pdf |
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Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Ciência da Computação em Rede UFG/UFMS (INF) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Informática - INF (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
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Universidade Federal de Goiás (UFG) |
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UFG |
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Repositório Institucional da UFG |
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Repositório Institucional da UFG |
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