Hierarchical learning using deep optimum-path forest

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dc.contributor.authorAfonso, Luis C.S.
dc.contributor.authorPereira, Clayton R. [UNESP]
dc.contributor.authorWeber, Silke A.T. [UNESP]
dc.contributor.authorHook, Christian
dc.contributor.authorFalcão, Alexandre X.
dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionOstbayerische Technische Hochschule
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2020-12-12T02:44:48Z
dc.date.available2020-12-12T02:44:48Z
dc.date.issued2020-08-01
dc.description.abstractBag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.en
dc.description.affiliationUFSCar - Federal University of São Carlos Department of Computing
dc.description.affiliationUNESP - São Paulo State University School of Sciences
dc.description.affiliationOstbayerische Technische Hochschule
dc.description.affiliationUNICAMP - University of Campinas Institute of Computing
dc.description.affiliationUnespUNESP - São Paulo State University School of Sciences
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: #2013/07375-0
dc.description.sponsorshipIdFAPESP: #2014/12236-1
dc.description.sponsorshipIdFAPESP: #2019/07665-4
dc.description.sponsorshipIdCNPq: #307066/2017-7
dc.description.sponsorshipIdCNPq: #427968/2018-6
dc.identifierhttp://dx.doi.org/10.1016/j.jvcir.2020.102823
dc.identifier.citationJournal of Visual Communication and Image Representation, v. 71.
dc.identifier.doi10.1016/j.jvcir.2020.102823
dc.identifier.issn1095-9076
dc.identifier.issn1047-3203
dc.identifier.scopus2-s2.0-85086903747
dc.identifier.urihttp://hdl.handle.net/11449/201903
dc.language.isoeng
dc.relation.ispartofJournal of Visual Communication and Image Representation
dc.sourceScopus
dc.subjectHandwriting dynamics
dc.subjectHierarchical representation
dc.subjectOptimum-path forest
dc.subjectParkinson's disease
dc.titleHierarchical learning using deep optimum-path foresten
dc.typeArtigo
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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Bauru - FC - Faculdade de Ciências