Optimizing Contextual-Based Optimum-Forest Classification through Swarm Intelligence

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Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorOsaku, Daniel-
Autor(es): dc.creatorNakamura, Rodrigo [UNESP]-
Autor(es): dc.creatorPapa, Joao [UNESP]-
Autor(es): dc.creatorLevada, Alexandre-
Autor(es): dc.creatorCappabianco, Fabio-
Autor(es): dc.creatorFalcao, Alexandre-
Autor(es): dc.creatorBlancTalon, J.-
Autor(es): dc.creatorKasinski, A.-
Autor(es): dc.creatorPhilips, W.-
Autor(es): dc.creatorPopescu, D.-
Autor(es): dc.creatorScheunders, P.-
Data de aceite: dc.date.accessioned2022-02-22T00:03:55Z-
Data de disponibilização: dc.date.available2022-02-22T00:03:55Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2013-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/194781-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/194781-
Descrição: dc.descriptionSeveral works have been conducted in order to improve classification problems. However, a considerable amount of them do not consider the contextual information in the learning process, which may help the classification step by providing additional information about the relation between a sample and its neighbourhood. Recently, a previous work have proposed a hybrid approach between Optimum-Path Forest classifier and Markov Random Fields (OPF-MRF) aiming to provide contextual information for this classifier. However, the contextual information was restricted to a spatial/temporal-dependent parameter, which has been empirically chosen in that work. We propose here an improvement of OPF-MRF by modelling the problem of finding such parameter as a swarm-based optimization task, which is carried out Particle Swarm Optimization and Harmony Search. The results have been conducted over the classification of Magnetic Ressonance Images of the brain, and the proposed approach seemed to find close results to the ones obtained by an exhaustive search for this parameter, but much faster for that.-
Descrição: dc.descriptionUniv Fed Sao Carlos, Dept Comp, BR-13560 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUniv Fed Sao Carlos, Inst Sci & Technol, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Dept Comp, Sao Paulo, Brazil-
Descrição: dc.descriptionUniv Estadual Campinas, Inst Comp, Campinas, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Dept Comp, Sao Paulo, Brazil-
Formato: dc.format203-214-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationAdvanced Concepts For Intelligent Vision Systems, Acivs 2013-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectMagnetic Resonance Images-
Palavras-chave: dc.subjectOptimum-Path Forest-
Palavras-chave: dc.subjectMarkov Random Fields-
Palavras-chave: dc.subjectParticle Swarm Optimization-
Palavras-chave: dc.subjectHarmony Search-
Título: dc.titleOptimizing Contextual-Based Optimum-Forest Classification through Swarm Intelligence-
Aparece nas coleções:Repositório Institucional - Unesp

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