O2PF: Oversampling via optimum-path forest for breast cancer detection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPassos, Leandro [UNESP]-
Autor(es): dc.creatorJodas, Danilo [UNESP]-
Autor(es): dc.creatorRibeiro, Luiz [UNESP]-
Autor(es): dc.creatorMoreira, Thierry [UNESP]-
Autor(es): dc.creatorPapa, Joao [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:08:55Z-
Data de disponibilização: dc.date.available2022-08-04T22:08:55Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/CBMS49503.2020.00100-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221556-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221556-
Descrição: dc.descriptionBreast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, they still commonly present low accuracies and demand considerable time and effort from professionals. Therefore, a computer-aided diagnosis (CAD) system capable of providing early detection becomes hugely desirable. In the last decade, machine learning-based techniques have been of paramount importance in this context, since they are capable of extracting essential information from data and reasoning about it. However, such approaches still suffer from imbalanced data, specifically on medical issues, where the number of healthy people samples is, in general, considerably higher than the number of patients. Therefore this paper proposes the O2PF, a data oversampling method based on the unsupervised Optimum-Path Forest Algorithm. Experiments conducted over the full oversampling scenario state the robustness of the model, which is compared against three well-established oversampling methods considering three breast cancer and three general-purpose tasks for medical issues datasets.-
Descrição: dc.descriptionSao Paulo State University Department of Computing-
Descrição: dc.descriptionSao Paulo State University Department of Computing-
Formato: dc.format498-503-
Idioma: dc.languageen-
Relação: dc.relationProceedings - IEEE Symposium on Computer-Based Medical Systems-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectData imbalance-
Palavras-chave: dc.subjectOptimum-path forest-
Palavras-chave: dc.subjectOversampling-
Título: dc.titleO2PF: Oversampling via optimum-path forest for breast cancer detection-
Aparece nas coleções:Repositório Institucional - Unesp

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