FEMa-FS: Finite Element Machines for Feature Selection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorAnalytics2Go-
Autor(es): dc.contributorUniversity of Wolverhampton-
Autor(es): dc.creatorBiaggi, Lucas-
Autor(es): dc.creatorPapa, Joao P.-
Autor(es): dc.creatorCosta, Kelton A. P-
Autor(es): dc.creatorPereira, Danillo R.-
Autor(es): dc.creatorPassos, Leandro A.-
Data de aceite: dc.date.accessioned2025-08-21T21:37:04Z-
Data de disponibilização: dc.date.available2025-08-21T21:37:04Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICPR56361.2022.9956112-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249455-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249455-
Descrição: dc.descriptionIdentifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.-
Descrição: dc.descriptionSão Paulo State University-
Descrição: dc.descriptionAnalytics2Go, Álvares Machado-
Descrição: dc.descriptionUniversity of Wolverhampton-
Descrição: dc.descriptionSão Paulo State University-
Formato: dc.format1784-1791-
Idioma: dc.languageen-
Relação: dc.relationProceedings - International Conference on Pattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComputer Networks Security-
Palavras-chave: dc.subjectFeature Selection-
Palavras-chave: dc.subjectFinite Element Method-
Palavras-chave: dc.subjectMachine Learning-
Título: dc.titleFEMa-FS: Finite Element Machines for Feature Selection-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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