Publicação: Particle Competition and Cooperation in Networks for Semi-Supervised Learning
dc.contributor.author | Breve, Fabricio Aparecido [UNESP] | |
dc.contributor.author | Zhao, Liang | |
dc.contributor.author | Quiles, Marcos | |
dc.contributor.author | Pedrycz, Witold | |
dc.contributor.author | Liu, Jiming | |
dc.contributor.institution | Universidade de São Paulo (USP) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Univ Alberta | |
dc.contributor.institution | Polish Acad Sci | |
dc.contributor.institution | Hong Kong Baptist Univ | |
dc.date.accessioned | 2013-09-30T18:50:25Z | |
dc.date.accessioned | 2014-05-20T14:16:18Z | |
dc.date.available | 2013-09-30T18:50:25Z | |
dc.date.available | 2014-05-20T14:16:18Z | |
dc.date.issued | 2012-09-01 | |
dc.description.abstract | Semi-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.affiliation | Univ São Paulo, Dept Computat, Inst Math & Comp Sci, BR-13566590 São Carlos, SP, Brazil | |
dc.description.affiliation | São Paulo State Univ UNESP, Dept Stat Appl Math & Computat DEMAC, Inst Geosci & Exact Sci IGCE, BR-13506900 Rio Claro, SP, Brazil | |
dc.description.affiliation | Fed Univ São Paulo Unifesp, Dept Sci & Technol DCT, BR-12231280 Sao Jose Dos Campos, SP, Brazil | |
dc.description.affiliation | Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada | |
dc.description.affiliation | Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland | |
dc.description.affiliation | Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China | |
dc.description.affiliationUnesp | São Paulo State Univ UNESP, Dept Stat Appl Math & Computat DEMAC, Inst Geosci & Exact Sci IGCE, BR-13506900 Rio Claro, SP, Brazil | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
dc.format.extent | 1686-1698 | |
dc.identifier | http://dx.doi.org/10.1109/TKDE.2011.119 | |
dc.identifier.citation | IEEE Transactions on Knowledge and Data Engineering. Los Alamitos: IEEE Computer Soc, v. 24, n. 9, p. 1686-1698, 2012. | |
dc.identifier.doi | 10.1109/TKDE.2011.119 | |
dc.identifier.issn | 1041-4347 | |
dc.identifier.lattes | 5693860025538327 | |
dc.identifier.orcid | 0000-0002-1123-9784 | |
dc.identifier.uri | http://hdl.handle.net/11449/24904 | |
dc.identifier.wos | WOS:000306557800011 | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE), Computer Soc | |
dc.relation.ispartof | IEEE Transactions on Knowledge and Data Engineering | |
dc.relation.ispartofjcr | 2.775 | |
dc.relation.ispartofsjr | 1,133 | |
dc.rights.accessRights | Acesso restrito | |
dc.source | Web of Science | |
dc.subject | Semi-supervised learning | en |
dc.subject | particles competition and cooperation | en |
dc.subject | network-based methods | en |
dc.subject | label propagation | en |
dc.title | Particle Competition and Cooperation in Networks for Semi-Supervised Learning | en |
dc.type | Artigo | |
dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
dcterms.rightsHolder | IEEE Computer Soc | |
dspace.entity.type | Publication | |
unesp.author.lattes | 5693860025538327[1] | |
unesp.author.orcid | 0000-0002-1123-9784[1] | |
unesp.campus | Universidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claro | pt |
unesp.department | Estatística, Matemática Aplicada e Computação - IGCE | pt |
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