Publicação:
Particle Competition and Cooperation in Networks for Semi-Supervised Learning

dc.contributor.authorBreve, Fabricio Aparecido [UNESP]
dc.contributor.authorZhao, Liang
dc.contributor.authorQuiles, Marcos
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLiu, Jiming
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Alberta
dc.contributor.institutionPolish Acad Sci
dc.contributor.institutionHong Kong Baptist Univ
dc.date.accessioned2013-09-30T18:50:25Z
dc.date.accessioned2014-05-20T14:16:18Z
dc.date.available2013-09-30T18:50:25Z
dc.date.available2014-05-20T14:16:18Z
dc.date.issued2012-09-01
dc.description.abstractSemi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a divide-and-conquer effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.en
dc.description.affiliationUniv São Paulo, Dept Computat, Inst Math & Comp Sci, BR-13566590 São Carlos, SP, Brazil
dc.description.affiliationSão Paulo State Univ UNESP, Dept Stat Appl Math & Computat DEMAC, Inst Geosci & Exact Sci IGCE, BR-13506900 Rio Claro, SP, Brazil
dc.description.affiliationFed Univ São Paulo Unifesp, Dept Sci & Technol DCT, BR-12231280 Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUniv Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada
dc.description.affiliationPolish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
dc.description.affiliationHong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
dc.description.affiliationUnespSão Paulo State Univ UNESP, Dept Stat Appl Math & Computat DEMAC, Inst Geosci & Exact Sci IGCE, BR-13506900 Rio Claro, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent1686-1698
dc.identifierhttp://dx.doi.org/10.1109/TKDE.2011.119
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering. Los Alamitos: IEEE Computer Soc, v. 24, n. 9, p. 1686-1698, 2012.
dc.identifier.doi10.1109/TKDE.2011.119
dc.identifier.issn1041-4347
dc.identifier.lattes5693860025538327
dc.identifier.orcid0000-0002-1123-9784
dc.identifier.urihttp://hdl.handle.net/11449/24904
dc.identifier.wosWOS:000306557800011
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE), Computer Soc
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering
dc.relation.ispartofjcr2.775
dc.relation.ispartofsjr1,133
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectSemi-supervised learningen
dc.subjectparticles competition and cooperationen
dc.subjectnetwork-based methodsen
dc.subjectlabel propagationen
dc.titleParticle Competition and Cooperation in Networks for Semi-Supervised Learningen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIEEE Computer Soc
dspace.entity.typePublication
unesp.author.lattes5693860025538327[1]
unesp.author.orcid0000-0002-1123-9784[1]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.departmentEstatística, Matemática Aplicada e Computação - IGCEpt

Arquivos

Licença do Pacote

Agora exibindo 1 - 2 de 2
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição: