Ensemble approaches for regression: A survey

Detalhes bibliográficos
Autor(a) principal: Jorge Freire de Sousa
Data de Publicação: 2012
Outros Autores: João Mendes Moreira, Carlos Manuel Soares, Alípio Jorge
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/2760
http://dx.doi.org/10.1145/2379776.2379786
Resumo: The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.
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spelling Ensemble approaches for regression: A surveyThe goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.2017-11-16T14:05:25Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/2760http://dx.doi.org/10.1145/2379776.2379786engJorge Freire de SousaJoão Mendes MoreiraCarlos Manuel SoaresAlípio Jorgeinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:07Zoai:repositorio.inesctec.pt:123456789/2760Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:42.487255Repositório 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 Ensemble approaches for regression: A survey
title Ensemble approaches for regression: A survey
spellingShingle Ensemble approaches for regression: A survey
Jorge Freire de Sousa
title_short Ensemble approaches for regression: A survey
title_full Ensemble approaches for regression: A survey
title_fullStr Ensemble approaches for regression: A survey
title_full_unstemmed Ensemble approaches for regression: A survey
title_sort Ensemble approaches for regression: A survey
author Jorge Freire de Sousa
author_facet Jorge Freire de Sousa
João Mendes Moreira
Carlos Manuel Soares
Alípio Jorge
author_role author
author2 João Mendes Moreira
Carlos Manuel Soares
Alípio Jorge
author2_role author
author
author
dc.contributor.author.fl_str_mv Jorge Freire de Sousa
João Mendes Moreira
Carlos Manuel Soares
Alípio Jorge
description The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-11-16T14:05:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/2760
http://dx.doi.org/10.1145/2379776.2379786
url http://repositorio.inesctec.pt/handle/123456789/2760
http://dx.doi.org/10.1145/2379776.2379786
dc.language.iso.fl_str_mv eng
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