A new parallel training algorithm for optimum-path forest-based learning

Detalhes bibliográficos
Autor(a) principal: Culquicondor, Aldo
Data de Publicação: 2017
Outros Autores: Castelo-Fernández, César, Papa, João Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-52277-7_24
http://hdl.handle.net/11449/178659
Resumo: In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness.
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spelling A new parallel training algorithm for optimum-path forest-based learningGraph algorithmsOptimum-path forestParallel algorithmsIn this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Escuela de Ciencia de la Computacion Universidad Catolica San PabloComputer Science Department Sao Paulo State University - UNESPComputer Science Department Sao Paulo State University - UNESPFAPESP: #2014/16250-9CNPq: #306166/2014-3CNPq: #470571/2013-6Universidad Catolica San PabloUniversidade Estadual Paulista (Unesp)Culquicondor, AldoCastelo-Fernández, CésarPapa, João Paulo [UNESP]2018-12-11T17:31:31Z2018-12-11T17:31:31Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject192-199http://dx.doi.org/10.1007/978-3-319-52277-7_24Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10125 LNCS, p. 192-199.1611-33490302-9743http://hdl.handle.net/11449/17865910.1007/978-3-319-52277-7_242-s2.0-85013418925Scopusreponame: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/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/178659Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:35:22.875279Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A new parallel training algorithm for optimum-path forest-based learning
title A new parallel training algorithm for optimum-path forest-based learning
spellingShingle A new parallel training algorithm for optimum-path forest-based learning
Culquicondor, Aldo
Graph algorithms
Optimum-path forest
Parallel algorithms
title_short A new parallel training algorithm for optimum-path forest-based learning
title_full A new parallel training algorithm for optimum-path forest-based learning
title_fullStr A new parallel training algorithm for optimum-path forest-based learning
title_full_unstemmed A new parallel training algorithm for optimum-path forest-based learning
title_sort A new parallel training algorithm for optimum-path forest-based learning
author Culquicondor, Aldo
author_facet Culquicondor, Aldo
Castelo-Fernández, César
Papa, João Paulo [UNESP]
author_role author
author2 Castelo-Fernández, César
Papa, João Paulo [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Catolica San Pablo
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Culquicondor, Aldo
Castelo-Fernández, César
Papa, João Paulo [UNESP]
dc.subject.por.fl_str_mv Graph algorithms
Optimum-path forest
Parallel algorithms
topic Graph algorithms
Optimum-path forest
Parallel algorithms
description In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
2018-12-11T17:31:31Z
2018-12-11T17:31:31Z
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/10.1007/978-3-319-52277-7_24
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10125 LNCS, p. 192-199.
1611-3349
0302-9743
http://hdl.handle.net/11449/178659
10.1007/978-3-319-52277-7_24
2-s2.0-85013418925
url http://dx.doi.org/10.1007/978-3-319-52277-7_24
http://hdl.handle.net/11449/178659
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10125 LNCS, p. 192-199.
1611-3349
0302-9743
10.1007/978-3-319-52277-7_24
2-s2.0-85013418925
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 192-199
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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