WB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorCenter for Monitoring and Early Warning of Natural Disasters (CEMADEN)-
Autor(es): dc.contributorNational Institute for Space Research (INPE)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBilla, Wagner S.-
Autor(es): dc.creatorNegri, Rogério G.-
Autor(es): dc.creatorSantos, Leonardo B. L.-
Data de aceite: dc.date.accessioned2025-08-21T15:57:14Z-
Data de disponibilização: dc.date.available2025-08-21T15:57:14Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/eng4040142-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304084-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304084-
Descrição: dc.descriptionThis article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly encountered: (i) Ensuring classifiers possess robustness to noise; (ii) Overcoming the difficulty of obtaining representative data that captures real-world noise; (iii) Addressing the complexity of detecting noise, making it challenging to differentiate it from natural variations in the data; and (iv) Meeting the requirement for classifiers capable of efficiently handling noise, allowing prompt responses for decision-making. WB Score provides a comprehensive approach for classifier assessment and selection to address these challenges. We analyze five classic datasets and one customized flooding dataset in São Paulo. The results demonstrate the practical effect of using the WB Score methodology is the enhanced ability to select robust classifiers for datasets in noisy real-world scenarios. Compared with similar techniques, the improvement centers around providing a visual and intuitive output, enhancing the understanding of classifier resilience against noise, and streamlining the decision-making process.-
Descrição: dc.descriptionCenter for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos-
Descrição: dc.descriptionNational Institute for Space Research (INPE), São José dos Campos-
Descrição: dc.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP), São José dos Campos-
Descrição: dc.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP), São José dos Campos-
Formato: dc.format2497-2513-
Idioma: dc.languageen-
Relação: dc.relationEng-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectclassifier selection-
Palavras-chave: dc.subjectcomputational classification-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectnoise robustness-
Palavras-chave: dc.subjectvisual decision-making-
Título: dc.titleWB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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