Random rules from data streams

Detalhes bibliográficos
Autor(a) principal: Ezilda Almeida
Data de Publicação: 2013
Outros Autores: Petr Kosina, João Gama
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/83804
Resumo: Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once. Copyright 2013 ACM.
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spelling Random rules from data streamsCiência de computadores, Ciências da computação e da informaçãoComputer science, Computer and information sciencesExisting works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once. Copyright 2013 ACM.20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/83804eng10.1145/2480362.2480518Ezilda AlmeidaPetr KosinaJoão 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:RCAAP2023-11-29T15:18:32Zoai:repositorio-aberto.up.pt:10216/83804Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:20:15.821679Repositório 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 Random rules from data streams
title Random rules from data streams
spellingShingle Random rules from data streams
Ezilda Almeida
Ciência de computadores, Ciências da computação e da informação
Computer science, Computer and information sciences
title_short Random rules from data streams
title_full Random rules from data streams
title_fullStr Random rules from data streams
title_full_unstemmed Random rules from data streams
title_sort Random rules from data streams
author Ezilda Almeida
author_facet Ezilda Almeida
Petr Kosina
João Gama
author_role author
author2 Petr Kosina
João Gama
author2_role author
author
dc.contributor.author.fl_str_mv Ezilda Almeida
Petr Kosina
João Gama
dc.subject.por.fl_str_mv Ciência de computadores, Ciências da computação e da informação
Computer science, Computer and information sciences
topic Ciência de computadores, Ciências da computação e da informação
Computer science, Computer and information sciences
description Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once. Copyright 2013 ACM.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
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dc.type.driver.fl_str_mv info:eu-repo/semantics/book
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/83804
url https://hdl.handle.net/10216/83804
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1145/2480362.2480518
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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)
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