Adaptive learning for dynamic environments: A comparative approach

Detalhes bibliográficos
Autor(a) principal: Costa,J
Data de Publicação: 2017
Outros Autores: Silva,C, Mário João Antunes, Ribeiro,B
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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spelling 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
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5239
http://dx.doi.org/10.1016/j.engappai.2017.08.004
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http://dx.doi.org/10.1016/j.engappai.2017.08.004
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