Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorPonti-, Moacir P.-
Autor(es): dc.creatorPapa, Joao P. [UNESP]-
Autor(es): dc.creatorSansone, C.-
Autor(es): dc.creatorKittler, J.-
Autor(es): dc.creatorRoli, F.-
Data de aceite: dc.date.accessioned2022-02-22T00:08:15Z-
Data de disponibilização: dc.date.available2022-02-22T00:08:15Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2011-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196019-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196019-
Descrição: dc.descriptionThe Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OFF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure, The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OFF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more.-
Descrição: dc.descriptionUniv Sao Paulo ICMC USP, Inst Math & Comp Sci, BR-13560970 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUNESP, Dept Comp, Bauru, SP, Brazil-
Descrição: dc.descriptionUNESP, Dept Comp, Bauru, SP, Brazil-
Formato: dc.format237-+-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationMultiple Classifier Systems-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectOptimum-Path Forest classifier-
Palavras-chave: dc.subjectdistributed combination of classifiers-
Palavras-chave: dc.subjectpasting small votes-
Título: dc.titleImproving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.