Multiple-Instance Learning through Optimum-Path Forest

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorPetróleo Brasileiro S.A.-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorAfonso, Luis C. S.-
Autor(es): dc.creatorColombo, Danilo-
Autor(es): dc.creatorPereira, Clayton R. [UNESP]-
Autor(es): dc.creatorCosta, Kelton A. P. [UNESP]-
Autor(es): dc.creatorPapa, Joao P. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:32:56Z-
Data de disponibilização: dc.date.available2022-02-22T00:32:56Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2019.8852454-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201222-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201222-
Descrição: dc.descriptionMultiple-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.-
Descrição: dc.descriptionDepartment of Computing UFSCar - Federal University of São Carlos-
Descrição: dc.descriptionCenpes Petróleo Brasileiro S.A.-
Descrição: dc.descriptionDepartment of Computing UNESP - São Paulo State University-
Descrição: dc.descriptionDepartment of Computing UNESP - São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Neural Networks-
???dc.source???: dc.sourceScopus-
Título: dc.titleMultiple-Instance Learning through Optimum-Path Forest-
Aparece nas coleções:Repositório Institucional - Unesp

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