The data replication method for the classification with reject option

Detalhes bibliográficos
Autor(a) principal: Sousa,R
Data de Publicação: 2013
Outros Autores: Jaime Cardoso
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
DOI: 10.3233/aic-130566
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/7173
http://dx.doi.org/10.3233/aic-130566
Resumo: Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we tailor a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real datasets verifies the usefulness of the proposed approach.
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spelling The data replication method for the classification with reject optionClassification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we tailor a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real datasets verifies the usefulness of the proposed approach.2018-01-21T15:47:32Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/7173http://dx.doi.org/10.3233/aic-130566engSousa,RJaime Cardosoinfo: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:RCAAP2024-10-12T02:21:09Zoai:repositorio.inesctec.pt:123456789/7173Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-10-12T02:21:09Repositório 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 The data replication method for the classification with reject option
title The data replication method for the classification with reject option
spellingShingle The data replication method for the classification with reject option
The data replication method for the classification with reject option
Sousa,R
Sousa,R
title_short The data replication method for the classification with reject option
title_full The data replication method for the classification with reject option
title_fullStr The data replication method for the classification with reject option
The data replication method for the classification with reject option
title_full_unstemmed The data replication method for the classification with reject option
The data replication method for the classification with reject option
title_sort The data replication method for the classification with reject option
author Sousa,R
author_facet Sousa,R
Sousa,R
Jaime Cardoso
Jaime Cardoso
author_role author
author2 Jaime Cardoso
author2_role author
dc.contributor.author.fl_str_mv Sousa,R
Jaime Cardoso
description Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we tailor a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real datasets verifies the usefulness of the proposed approach.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2018-01-21T15:47:32Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/7173
http://dx.doi.org/10.3233/aic-130566
url http://repositorio.inesctec.pt/handle/123456789/7173
http://dx.doi.org/10.3233/aic-130566
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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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
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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
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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dc.identifier.doi.none.fl_str_mv 10.3233/aic-130566