An incremental Optimum-Path Forest classifier and its application to non-technical losses identification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorIwashita, Adriana Sayuri-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorGastaldello, Danilo Sinkiti-
Autor(es): dc.creatorde Souza, Andre Nunes-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T17:23:27Z-
Data de disponibilização: dc.date.available2025-08-21T17:23:27Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2021-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.compeleceng.2021.107389-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233470-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233470-
Descrição: dc.descriptionNon-technical losses stand for the energy consumed but not billed, affecting the energy grid as a whole. Such an issue somehow prevails in developing countries, harming the quality of energy and preventing social programs benefit from tax revenues. Machine learning techniques can help mitigate it by mining information from fraudsters and legal users for further decision-making. In this paper, we deal with a steady increase of dataset size, i.e., the incremental learning problem, which can cope with datasets regularly provided by energy companies, requiring the learner to be updated constantly. Since repeating the entire learning process might be prohibitive, adjusting the model to the new data shows to be a better choice. We propose an incremental Optimum-Path Forest approach with k-nn neighborhood that is considerably more efficient for training than its counterpart version, with experiments validated in general-purpose datasets and also in the context of non-technical losses identification.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Computing Federal University of São Carlos, Rod. Washington Luís, km 235-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionFAPESP: #2013/07375-0-
Descrição: dc.descriptionFAPESP: #2014/12236-1-
Descrição: dc.descriptionFAPESP: #2017/02286-0-
Descrição: dc.descriptionFAPESP: #2018/21934-5-
Descrição: dc.descriptionFAPESP: #2019/07665-4-
Descrição: dc.descriptionCNPq: #307066/2017-7-
Descrição: dc.descriptionCNPq: #427968/2018-6-
Idioma: dc.languageen-
Relação: dc.relationComputers and Electrical Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCommercial losses-
Palavras-chave: dc.subjectIncremental learning-
Palavras-chave: dc.subjectNon-technical losses-
Palavras-chave: dc.subjectOptimum-path forest-
Título: dc.titleAn incremental Optimum-Path Forest classifier and its application to non-technical losses identification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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