Combined active and semi-supervised learning using particle walking temporal dynamics
| dc.contributor.author | Breve, Fabricio [UNESP] | |
| dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
| dc.date.accessioned | 2015-03-18T15:55:03Z | |
| dc.date.available | 2015-03-18T15:55:03Z | |
| dc.date.issued | 2013-01-01 | |
| dc.description.abstract | Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them. | en |
| dc.description.affiliation | Sao Paulo State Univ UNESP, Inst Geosci & Exact Sci IGCE, Rio Claro, Brazil | |
| dc.description.affiliationUnesp | Sao Paulo State Univ UNESP, Inst Geosci & Exact Sci IGCE, Rio Claro, Brazil | |
| dc.format.extent | 15-20 | |
| dc.identifier | http://dx.doi.org/10.1109/BRICS-CCI-CBIC.2013.14 | |
| dc.identifier.citation | 2013 1st Brics Countries Congress On Computational Intelligence And 11th Brazilian Congress On Computational Intelligence (brics-cci & Cbic). New York: Ieee, p. 15-20, 2013. | |
| dc.identifier.doi | 10.1109/BRICS-CCI-CBIC.2013.14 | |
| dc.identifier.lattes | 5693860025538327 | |
| dc.identifier.orcid | 0000-0002-1123-9784 | |
| dc.identifier.uri | http://hdl.handle.net/11449/117067 | |
| dc.identifier.wos | WOS:000346422500003 | |
| dc.language.iso | eng | |
| dc.publisher | Ieee | |
| dc.relation.ispartof | 2013 1st Brics Countries Congress On Computational Intelligence And 11th Brazilian Congress On Computational Intelligence (brics-cci & Cbic) | |
| dc.rights.accessRights | Acesso aberto | |
| dc.source | Web of Science | |
| dc.title | Combined active and semi-supervised learning using particle walking temporal dynamics | en |
| dc.type | Trabalho apresentado em evento | |
| dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
| dcterms.rightsHolder | Ieee | |
| unesp.author.lattes | 5693860025538327 | |
| unesp.campus | Universidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claro | pt |