A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

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dc.contributor.authorSouza, Renato William R. de
dc.contributor.authorOliveira, Joao Vitor Chaves de
dc.contributor.authorPassos, Leandro A. [UNESP]
dc.contributor.authorDing, Weiping
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorAlbuquerque, Victor Hugo C. de
dc.contributor.institutionUniv Fortaleza
dc.contributor.institutionPontificia Univ Catolica Rio de Janeiro
dc.contributor.institutionPontifical Catholic Univ Rio de Janeiro
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionNantong Univ
dc.date.accessioned2021-06-25T22:04:47Z
dc.date.available2021-06-25T22:04:47Z
dc.date.issued2020-12-01
dc.description.abstractIn the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for supervised, semisupervised, and unsupervised learning, named optimum-path forest (OPF), was proposed with competitive results in several applications, besides comprising a low computational burden. In this article, we propose the fuzzy OPF, an improved version of the standard OPF classifier, that learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over 12 public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst case scenarios.en
dc.description.affiliationUniv Fortaleza, Grad Program Appl Informat, BR-60811905 Fortaleza, Ceara, Brazil
dc.description.affiliationPontificia Univ Catolica Rio de Janeiro, BR-22451900 Rio De Janeiro, Brazil
dc.description.affiliationPontifical Catholic Univ Rio de Janeiro, BR-22451900 Rio De Janeiro, Brazil
dc.description.affiliationSao Paulo State Univ, BR-01049010 Sao Paulo, Brazil
dc.description.affiliationNantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China
dc.description.affiliationUnespSao Paulo State Univ, BR-01049010 Sao Paulo, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipNational Natural Science Foundation of China
dc.description.sponsorshipNatural Science Foundation of Jiangsu Province
dc.description.sponsorshipSix Talent Peaks Project of Jiangsu Province
dc.description.sponsorshipQing Lan Project of Jiangsu Province
dc.description.sponsorshipIdCNPq: 304315/2017-6
dc.description.sponsorshipIdCNPq: 427968/2018-6
dc.description.sponsorshipIdCNPq: 430274/2018-1
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2017/25908-6
dc.description.sponsorshipIdFAPESP: 2018/21934-5
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdNational Natural Science Foundation of China: 61976120
dc.description.sponsorshipIdNatural Science Foundation of Jiangsu Province: BK20191445
dc.description.sponsorshipIdSix Talent Peaks Project of Jiangsu Province: XYDXXJS-048
dc.format.extent3076-3086
dc.identifierhttp://dx.doi.org/10.1109/TFUZZ.2019.2949771
dc.identifier.citationIeee Transactions On Fuzzy Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 28, n. 12, p. 3076-3086, 2020.
dc.identifier.doi10.1109/TFUZZ.2019.2949771
dc.identifier.issn1063-6706
dc.identifier.urihttp://hdl.handle.net/11449/210572
dc.identifier.wosWOS:000595527100003
dc.language.isoeng
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Fuzzy Systems
dc.sourceWeb of Science
dc.subjectTraining
dc.subjectPrototypes
dc.subjectForestry
dc.subjectStandards
dc.subjectSupport vector machines
dc.subjectFuzzy logic
dc.subjectClustering algorithms
dc.subjectClassifiers
dc.subjectfuzzy
dc.subjectoptimum-path forest (OPF)
dc.subjectpattern recognition
dc.titleA Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logicen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee-inst Electrical Electronics Engineers Inc
unesp.author.orcid0000-0003-0517-8775[2]
unesp.author.orcid0000-0003-3886-4309[6]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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