Active consensus-based semi-supervised growing neural gas
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-46672-9_15 http://hdl.handle.net/11449/169070 |
Resumo: | In this paper, we propose a new active semi-supervised growing neural gas (GNG) model, named Active Consensus-Based Semi-Supervised GNG, or ACSSGNG. This model extends the former CSSGNG model by introducing an active mechanism for querying more representative samples in comparison to a random, or passive, selection. Moreover, as a semi-supervised model, the ACSSGNG takes both labelled and unlabelled samples in the training procedure. In comparison to other adaptations of the GNG to semi-supervised classification, the ACSSGNG does not assign a single scalar label value to each neuron. Instead, a vector containing the representativeness level of each class is associated with each neuron. Here, this information is used to select which sample the specialist might label instead of using a random selection of samples. Computer experiments show that our model can deliver, on average, better classification results than state-of-art semi-supervised algorithms, including the CSSGNG. |
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Repositório Institucional da UNESP |
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Active consensus-based semi-supervised growing neural gasIn this paper, we propose a new active semi-supervised growing neural gas (GNG) model, named Active Consensus-Based Semi-Supervised GNG, or ACSSGNG. This model extends the former CSSGNG model by introducing an active mechanism for querying more representative samples in comparison to a random, or passive, selection. Moreover, as a semi-supervised model, the ACSSGNG takes both labelled and unlabelled samples in the training procedure. In comparison to other adaptations of the GNG to semi-supervised classification, the ACSSGNG does not assign a single scalar label value to each neuron. Instead, a vector containing the representativeness level of each class is associated with each neuron. Here, this information is used to select which sample the specialist might label instead of using a random selection of samples. Computer experiments show that our model can deliver, on average, better classification results than state-of-art semi-supervised algorithms, including the CSSGNG.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal University of S�o Paulo (UNIFESP)S�o Paulo State University (UNESP)S�o Paulo State University (UNESP)CNPq: 2011/17396-9CNPq: 2011/18496-7CNPq: 2015/21660-4Universidade Federal de São Paulo (UNIFESP)Universidade Estadual Paulista (Unesp)M�ximo, Vin�cius R.Nascimento, Mari� C. V.Breve, Fabricio A. [UNESP]Quiles, Marcos G.2018-12-11T16:44:13Z2018-12-11T16:44:13Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject126-135http://dx.doi.org/10.1007/978-3-319-46672-9_15Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9948 LNCS, p. 126-135.1611-33490302-9743http://hdl.handle.net/11449/16907010.1007/978-3-319-46672-9_152-s2.0-84992623341Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:30Zoai:repositorio.unesp.br:11449/169070Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:29:40.332779Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Active consensus-based semi-supervised growing neural gas |
title |
Active consensus-based semi-supervised growing neural gas |
spellingShingle |
Active consensus-based semi-supervised growing neural gas M�ximo, Vin�cius R. |
title_short |
Active consensus-based semi-supervised growing neural gas |
title_full |
Active consensus-based semi-supervised growing neural gas |
title_fullStr |
Active consensus-based semi-supervised growing neural gas |
title_full_unstemmed |
Active consensus-based semi-supervised growing neural gas |
title_sort |
Active consensus-based semi-supervised growing neural gas |
author |
M�ximo, Vin�cius R. |
author_facet |
M�ximo, Vin�cius R. Nascimento, Mari� C. V. Breve, Fabricio A. [UNESP] Quiles, Marcos G. |
author_role |
author |
author2 |
Nascimento, Mari� C. V. Breve, Fabricio A. [UNESP] Quiles, Marcos G. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Paulo (UNIFESP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
M�ximo, Vin�cius R. Nascimento, Mari� C. V. Breve, Fabricio A. [UNESP] Quiles, Marcos G. |
description |
In this paper, we propose a new active semi-supervised growing neural gas (GNG) model, named Active Consensus-Based Semi-Supervised GNG, or ACSSGNG. This model extends the former CSSGNG model by introducing an active mechanism for querying more representative samples in comparison to a random, or passive, selection. Moreover, as a semi-supervised model, the ACSSGNG takes both labelled and unlabelled samples in the training procedure. In comparison to other adaptations of the GNG to semi-supervised classification, the ACSSGNG does not assign a single scalar label value to each neuron. Instead, a vector containing the representativeness level of each class is associated with each neuron. Here, this information is used to select which sample the specialist might label instead of using a random selection of samples. Computer experiments show that our model can deliver, on average, better classification results than state-of-art semi-supervised algorithms, including the CSSGNG. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-12-11T16:44:13Z 2018-12-11T16:44:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-319-46672-9_15 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9948 LNCS, p. 126-135. 1611-3349 0302-9743 http://hdl.handle.net/11449/169070 10.1007/978-3-319-46672-9_15 2-s2.0-84992623341 |
url |
http://dx.doi.org/10.1007/978-3-319-46672-9_15 http://hdl.handle.net/11449/169070 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9948 LNCS, p. 126-135. 1611-3349 0302-9743 10.1007/978-3-319-46672-9_15 2-s2.0-84992623341 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
126-135 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128660250558464 |