A Multi-Class Probabilistic Optimum-Path Forest

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity Wolverhampton-
Autor(es): dc.creatorNachif Fernandes, Silas E.-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorJodas, Danilo-
Autor(es): dc.creatorAkio, Marco-
Autor(es): dc.creatorSouza, André N.-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T22:31:56Z-
Data de disponibilização: dc.date.available2025-08-21T22:31:56Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0011597700003417-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309143-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309143-
Descrição: dc.descriptionThe advent of machine learning provided numerous benefits to humankind, impacting fields such as medicine, military, and entertainment, to cite a few. In most cases, given some instances from a previously known domain, the intelligent algorithm is encharged of predicting a label that categorizes such samples in some learned context. Among several techniques capable of accomplishing such classification tasks, one may refer to Support Vector Machines, Neural Networks, or graph-based classifiers, such as the Optimum-Path Forest (OPF). Even though such a paradigm satisfies a wide sort of problems, others require the predicted class label and the classifier’s confidence, i.e., how sure the model is while attributing labels. Recently, an OPF-based variant was proposed to tackle this problem, i.e., the Probabilistic Optimum-Path Forest. Despite its satisfactory results over a considerable number of datasets, it was conceived to deal with binary classification only, thus lacking in the context of multi-class problems. Therefore, this paper proposes the Multi-Class Probabilistic Optimum-Path Forest, an extension designed to outdraw limitations observed in the standard Probabilistic OPF.-
Descrição: dc.descriptionEngineering and Physical Sciences Research Council-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionSchool of Engineering and Informatics University Wolverhampton-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University-
Formato: dc.format361-368-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMulti-Class-
Palavras-chave: dc.subjectOptimum-Path Forest-
Palavras-chave: dc.subjectProbabilistic Classification-
Título: dc.titleA Multi-Class Probabilistic Optimum-Path Forest-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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