Collaborative Filtering Matches Decision Templates: A Practical Approach to Estimate Predictions

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMartins, Guilherme Brandao-
Autor(es): dc.creatorPapa, Joao Paulo-
Data de aceite: dc.date.accessioned2025-08-21T18:31:54Z-
Data de disponibilização: dc.date.available2025-08-21T18:31:54Z-
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/SIBGRAPI55357.2022.9991773-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248220-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248220-
Descrição: dc.descriptionCollaborative Filtering stands as an underlying strategy to reasonably deal with large-scale problems like scalability and high sparsity. In the classifier fusion context, one could benefit from adopting such a strategy to learn decision templates effectively for the sake of computation efficiency. This paper introduces a framework that explores collaborative filtering-based latent factors models for fast decision template generation, assuming it has a sparse matrix structure. Experiments conducted over five general-purpose public datasets and statistically assessed have demonstrated its feasibility for building decision templates under low sparsity conditions and datasets labeled with fewer classes. Under such conditions, the proposed framework showed competitive recognition rates, significantly reducing computational costs, particularly when distance-based classifiers are employed for ensemble learning purposes.-
Descrição: dc.descriptionFederal University of São Carlos - UFSCar Department of Computing, São Carlos-
Descrição: dc.descriptionSão Paulo State University - Unesp Department of Computing, Bauru-
Descrição: dc.descriptionSão Paulo State University - Unesp Department of Computing, Bauru-
Formato: dc.format186-191-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022-
???dc.source???: dc.sourceScopus-
Título: dc.titleCollaborative Filtering Matches Decision Templates: A Practical Approach to Estimate Predictions-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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