Regression by Re-Ranking

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorWageningen University and Research-
Autor(es): dc.contributorNorwegian University of Science and Technology-
Autor(es): dc.creatorGonçalves, Filipe Marcel Fernandes-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Autor(es): dc.creatorda Silva Torres, Ricardo-
Data de aceite: dc.date.accessioned2025-08-21T17:03:08Z-
Data de disponibilização: dc.date.available2025-08-21T17:03:08Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2023.109577-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/247111-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/247111-
Descrição: dc.descriptionSeveral approaches based on regression have been developed in the past few years with the goal of improving prediction results, including the use of ranking strategies. Re-ranking has been exploited and successfully employed in several applications, improving rankings by encoding the manifold structure and redefining distances among elements from a dataset. Despite the promising results observed, re-ranking has not been evaluated in regressions tasks. This paper proposes a novel, generic, and customizable framework entitled Regression by Re-ranking (RbR), which explores the ability of re-ranking algorithms in determining relevant rankings of objects in prediction tasks. The framework relies on the integration of a base regressor, unsupervised re-ranking learning techniques, and predictions associated with nearest neighbours weighted according to their ranking positions. The RbR framework was evaluated under a rigorous experimental protocol and presented significant results in improving the prediction when compared to state-of-the-art approaches.-
Descrição: dc.descriptionInstitute of Computing (IC) University of Campinas (UNICAMP)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing São Paulo State University (UNESP)-
Descrição: dc.descriptionFarm Technology Group and Wageningen Data Competence Center Wageningen University and Research-
Descrição: dc.descriptionDepartment of ICT and Natural Sciences Norwegian University of Science and Technology-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectManifold-
Palavras-chave: dc.subjectPrediction-
Palavras-chave: dc.subjectRe-ranking-
Palavras-chave: dc.subjectRegression-
Palavras-chave: dc.subjectUnsupervised learning-
Título: dc.titleRegression by Re-Ranking-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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