Adaptive learning for dynamic environments: A comparative approach
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
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/123456789/5239 http://dx.doi.org/10.1016/j.engappai.2017.08.004 |
Resumo: | Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART). |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Adaptive learning for dynamic environments: A comparative approachNowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).2018-01-02T15:39:17Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5239http://dx.doi.org/10.1016/j.engappai.2017.08.004engCosta,JSilva,CMário João AntunesRibeiro,Binfo: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:RCAAP2023-05-15T10:20:08Zoai:repositorio.inesctec.pt:123456789/5239Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:42.896157Repositório 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 |
Adaptive learning for dynamic environments: A comparative approach |
title |
Adaptive learning for dynamic environments: A comparative approach |
spellingShingle |
Adaptive learning for dynamic environments: A comparative approach Costa,J |
title_short |
Adaptive learning for dynamic environments: A comparative approach |
title_full |
Adaptive learning for dynamic environments: A comparative approach |
title_fullStr |
Adaptive learning for dynamic environments: A comparative approach |
title_full_unstemmed |
Adaptive learning for dynamic environments: A comparative approach |
title_sort |
Adaptive learning for dynamic environments: A comparative approach |
author |
Costa,J |
author_facet |
Costa,J Silva,C Mário João Antunes Ribeiro,B |
author_role |
author |
author2 |
Silva,C Mário João Antunes Ribeiro,B |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Costa,J Silva,C Mário João Antunes Ribeiro,B |
description |
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART). |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-02T15:39:17Z |
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://repositorio.inesctec.pt/handle/123456789/5239 http://dx.doi.org/10.1016/j.engappai.2017.08.004 |
url |
http://repositorio.inesctec.pt/handle/123456789/5239 http://dx.doi.org/10.1016/j.engappai.2017.08.004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131602728517632 |