MINAS: multiclass learning algorithm for novelty detection in data streams
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/123456789/5309 http://dx.doi.org/10.1007/s10618-015-0433-y |
Resumo: | Data stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear or evolve over time. Most of the works found in the ND literature presents it as a binary classification task. In several data stream real life problems, ND must be treated as a multiclass task, in which, the known concept is composed by one or more classes and different new classes may appear. This work proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a multiclass task. In the initial training phase, MINAS builds a decision model based on a labeled data set. In the online phase, new examples are classified using this model, or marked as unknown. Groups of unknown examples can be used later to create valid novelty patterns (NP), which are added to the current model. The decision model is updated as new data come over the stream in order to reflect changes in the known classes and allow the addition of NP. This work also presents a set of experiments carried out comparing MINAS and the main novelty detection algorithms found in the literature, using artificial and real data sets. The experimental results show the potential of the proposed algorithm. |
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7160 |
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MINAS: multiclass learning algorithm for novelty detection in data streamsData stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear or evolve over time. Most of the works found in the ND literature presents it as a binary classification task. In several data stream real life problems, ND must be treated as a multiclass task, in which, the known concept is composed by one or more classes and different new classes may appear. This work proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a multiclass task. In the initial training phase, MINAS builds a decision model based on a labeled data set. In the online phase, new examples are classified using this model, or marked as unknown. Groups of unknown examples can be used later to create valid novelty patterns (NP), which are added to the current model. The decision model is updated as new data come over the stream in order to reflect changes in the known classes and allow the addition of NP. This work also presents a set of experiments carried out comparing MINAS and the main novelty detection algorithms found in the literature, using artificial and real data sets. The experimental results show the potential of the proposed algorithm.2018-01-03T10:35:07Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5309http://dx.doi.org/10.1007/s10618-015-0433-yengde Faria,ERde Leon Ferreira Carvalho,ACPDFJoão Gamainfo: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:RCAAP2023-05-15T10:20:25Zoai:repositorio.inesctec.pt:123456789/5309Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:04.736535Repositório 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 |
MINAS: multiclass learning algorithm for novelty detection in data streams |
title |
MINAS: multiclass learning algorithm for novelty detection in data streams |
spellingShingle |
MINAS: multiclass learning algorithm for novelty detection in data streams de Faria,ER |
title_short |
MINAS: multiclass learning algorithm for novelty detection in data streams |
title_full |
MINAS: multiclass learning algorithm for novelty detection in data streams |
title_fullStr |
MINAS: multiclass learning algorithm for novelty detection in data streams |
title_full_unstemmed |
MINAS: multiclass learning algorithm for novelty detection in data streams |
title_sort |
MINAS: multiclass learning algorithm for novelty detection in data streams |
author |
de Faria,ER |
author_facet |
de Faria,ER de Leon Ferreira Carvalho,ACPDF João Gama |
author_role |
author |
author2 |
de Leon Ferreira Carvalho,ACPDF João Gama |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
de Faria,ER de Leon Ferreira Carvalho,ACPDF João Gama |
description |
Data stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear or evolve over time. Most of the works found in the ND literature presents it as a binary classification task. In several data stream real life problems, ND must be treated as a multiclass task, in which, the known concept is composed by one or more classes and different new classes may appear. This work proposes MINAS, an algorithm for ND in data streams. MINAS deals with ND as a multiclass task. In the initial training phase, MINAS builds a decision model based on a labeled data set. In the online phase, new examples are classified using this model, or marked as unknown. Groups of unknown examples can be used later to create valid novelty patterns (NP), which are added to the current model. The decision model is updated as new data come over the stream in order to reflect changes in the known classes and allow the addition of NP. This work also presents a set of experiments carried out comparing MINAS and the main novelty detection algorithms found in the literature, using artificial and real data sets. The experimental results show the potential of the proposed algorithm. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01T00:00:00Z 2016 2018-01-03T10:35:07Z |
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/123456789/5309 http://dx.doi.org/10.1007/s10618-015-0433-y |
url |
http://repositorio.inesctec.pt/handle/123456789/5309 http://dx.doi.org/10.1007/s10618-015-0433-y |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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