MC-SQ and MC-MQ: Ensembles for Multi-Class Quantification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorSchool of Computer Science and Engineering-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorDonyavi, Zahra-
Autor(es): dc.creatorSerapiao, Adriane B. S.-
Autor(es): dc.creatorBatista, Gustavo-
Data de aceite: dc.date.accessioned2025-08-21T20:46:44Z-
Data de disponibilização: dc.date.available2025-08-21T20:46:44Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TKDE.2024.3372011-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307511-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307511-
Descrição: dc.descriptionQuantification research proposes methods to estimate the class distribution in an independent sample. Quantification methods find applications in areas that rely on estimated aggregated quantities, such as epidemiology, sentiment analysis, political research, and ecological surveillance. For instance, epidemiologists are often concerned with the dynamics of the number of disease cases across space and time. Thus, while classification predicts individual subjects, quantifiers are the methods that directly estimate the number of cases. Although quantification is a thriving area of research, with numerous approaches proposed in the last decade, most focus has been on binary-class quantifiers. One common approach for multi-class quantification is the one-versus-all (OVA) approach, but empirical evidence suggests its performance is suboptimal. This paper's first contribution is to elucidate why OVA quantifiers struggle to perform well in multi-class settings due to a distribution shift. To circumvent this problem, our second proposal is two new multi-class quantifiers based on ensemble learning that significantly improve performance for binary and multi-class settings. Our comprehensive experimental setup with 37 state-of-the-art (single and ensemble) quantifiers shows that our ensembles are the best-performing quantifiers and rank first in a recent quantification competition.-
Descrição: dc.descriptionUniversity of New South Wales School of Computer Science and Engineering-
Descrição: dc.descriptionSão Paulo State University-
Descrição: dc.descriptionSão Paulo State University-
Formato: dc.format4007-4019-
Idioma: dc.languageen-
Relação: dc.relationIEEE Transactions on Knowledge and Data Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClass probability estimation-
Palavras-chave: dc.subjectensembles-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmulti-class-
Palavras-chave: dc.subjectprevalence estimation-
Palavras-chave: dc.subjectquantification-
Título: dc.titleMC-SQ and MC-MQ: Ensembles for Multi-Class Quantification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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