Increasing the effectiveness of active learning

Detalhes bibliográficos
Autor(a) principal: Fonseca, Joao
Data de Publicação: 2021
Outros Autores: Douzas, Georgios, Bacao, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/121781
Resumo: Fonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619
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spelling Increasing the effectiveness of active learningIntroducing artificial data generation in active learning for land use/land cover classificationActive learningArtificial data generationLand use/land cover classificationOversamplingSMOTEEarth and Planetary Sciences(all)SDG 15 - Life on LandFonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNFonseca, JoaoDouzas, GeorgiosBacao, Fernando2021-07-28T23:28:02Z2021-07-012021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/121781eng2072-4292PURE: 32875378https://doi.org/10.3390/rs13132619info: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:RCAAP2024-03-11T05:03:55Zoai:run.unl.pt:10362/121781Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:43.269825Repositório 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 Increasing the effectiveness of active learning
Introducing artificial data generation in active learning for land use/land cover classification
title Increasing the effectiveness of active learning
spellingShingle Increasing the effectiveness of active learning
Fonseca, Joao
Active learning
Artificial data generation
Land use/land cover classification
Oversampling
SMOTE
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
title_short Increasing the effectiveness of active learning
title_full Increasing the effectiveness of active learning
title_fullStr Increasing the effectiveness of active learning
title_full_unstemmed Increasing the effectiveness of active learning
title_sort Increasing the effectiveness of active learning
author Fonseca, Joao
author_facet Fonseca, Joao
Douzas, Georgios
Bacao, Fernando
author_role author
author2 Douzas, Georgios
Bacao, Fernando
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Fonseca, Joao
Douzas, Georgios
Bacao, Fernando
dc.subject.por.fl_str_mv Active learning
Artificial data generation
Land use/land cover classification
Oversampling
SMOTE
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
topic Active learning
Artificial data generation
Land use/land cover classification
Oversampling
SMOTE
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
description Fonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619
publishDate 2021
dc.date.none.fl_str_mv 2021-07-28T23:28:02Z
2021-07-01
2021-07-01T00:00:00Z
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/10362/121781
url http://hdl.handle.net/10362/121781
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
PURE: 32875378
https://doi.org/10.3390/rs13132619
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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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