Multiple-Instance Learning through Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/196859 |
Resumo: | Multiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In binary-driven MI problems, the entire bag is considered positive if one (at least) sample is labeled as positive. On the other hand, a bag is considered negative if it contains all samples labeled as negative as well. In this paper, we introduced the Optimum-Path Forest (OPF) classifier to the context of multiple-instance learning paradigm, and we evaluated it in different scenarios that range from molecule description, text categorization, and anomaly detection in well-drilling report classification. The experimental results showed that two different OPF classifiers are very much suitable to handle problems in the multiple-instance learning paradigm. |
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Repositório Institucional da UNESP |
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Multiple-Instance Learning through Optimum-Path ForestMultiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In binary-driven MI problems, the entire bag is considered positive if one (at least) sample is labeled as positive. On the other hand, a bag is considered negative if it contains all samples labeled as negative as well. In this paper, we introduced the Optimum-Path Forest (OPF) classifier to the context of multiple-instance learning paradigm, and we evaluated it in different scenarios that range from molecule description, text categorization, and anomaly detection in well-drilling report classification. The experimental results showed that two different OPF classifiers are very much suitable to handle problems in the multiple-instance learning paradigm.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)PetrobrasUFSCar Fed Univ Selo Carlos, Dept Comp, Sao Carlos, BrazilPetr Brasileiro SA, Cenpes, Rio De Janeiro, RJ, BrazilUNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, BrazilUNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, BrazilFAPESP: 2013/07375-0FAPESP: 2014/122361FAPESP: 2016/19403-6FAPESP: 2017/22905-6CNPq: 307066/2017-7CNPq: 427968/2018-6Petrobras: 2014/00545-0IeeeUniversidade Federal de São Carlos (UFSCar)Petr Brasileiro SAUniversidade Estadual Paulista (Unesp)Afonso, Luis C. S.Colombo, DaniloPereira, Clayton R. [UNESP]Costa, Kelton A. P. [UNESP]Papa, Joao P. [UNESP]IEEE2020-12-10T19:58:25Z2020-12-10T19:58:25Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject72019 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 7 p., 2019.2161-4393http://hdl.handle.net/11449/196859WOS:000530893806001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2019 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2021-10-23T08:53:50Zoai:repositorio.unesp.br:11449/196859Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T08:53:50Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multiple-Instance Learning through Optimum-Path Forest |
title |
Multiple-Instance Learning through Optimum-Path Forest |
spellingShingle |
Multiple-Instance Learning through Optimum-Path Forest Afonso, Luis C. S. |
title_short |
Multiple-Instance Learning through Optimum-Path Forest |
title_full |
Multiple-Instance Learning through Optimum-Path Forest |
title_fullStr |
Multiple-Instance Learning through Optimum-Path Forest |
title_full_unstemmed |
Multiple-Instance Learning through Optimum-Path Forest |
title_sort |
Multiple-Instance Learning through Optimum-Path Forest |
author |
Afonso, Luis C. S. |
author_facet |
Afonso, Luis C. S. Colombo, Danilo Pereira, Clayton R. [UNESP] Costa, Kelton A. P. [UNESP] Papa, Joao P. [UNESP] IEEE |
author_role |
author |
author2 |
Colombo, Danilo Pereira, Clayton R. [UNESP] Costa, Kelton A. P. [UNESP] Papa, Joao P. [UNESP] IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Petr Brasileiro SA Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Afonso, Luis C. S. Colombo, Danilo Pereira, Clayton R. [UNESP] Costa, Kelton A. P. [UNESP] Papa, Joao P. [UNESP] IEEE |
description |
Multiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In binary-driven MI problems, the entire bag is considered positive if one (at least) sample is labeled as positive. On the other hand, a bag is considered negative if it contains all samples labeled as negative as well. In this paper, we introduced the Optimum-Path Forest (OPF) classifier to the context of multiple-instance learning paradigm, and we evaluated it in different scenarios that range from molecule description, text categorization, and anomaly detection in well-drilling report classification. The experimental results showed that two different OPF classifiers are very much suitable to handle problems in the multiple-instance learning paradigm. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-10T19:58:25Z 2020-12-10T19:58:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2019 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 7 p., 2019. 2161-4393 http://hdl.handle.net/11449/196859 WOS:000530893806001 |
identifier_str_mv |
2019 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 7 p., 2019. 2161-4393 WOS:000530893806001 |
url |
http://hdl.handle.net/11449/196859 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2019 International Joint Conference On Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
7 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1792961944447090688 |