Information Ranking Using Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN48605.2020.9207689 http://hdl.handle.net/11449/208066 |
Resumo: | The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking accurately and learning to rank are crucial tasks. Context-Based Information Retrieval systems have been of great importance to reduce the effort of finding relevant data. Such systems have evolved by using machine learning techniques to improve their results, but they are mainly dependent on user feedback. Although information retrieval has been addressed in different works along with classifiers based on Optimum-Path Forest (OPF), these have so far not been applied to the learning to rank task. Therefore, the main contribution of this work is to evaluate classifiers based on Optimum-Path Forest, in such a context. Experiments were performed considering the image retrieval and ranking scenarios, and the performance of OPF-based approaches was compared to the well-known SVM-Rank pairwise technique and a baseline based on distance calculation. The experiments showed competitive results concerning precision and outperformed traditional techniques in terms of computational load. |
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Repositório Institucional da UNESP |
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Information Ranking Using Optimum-Path ForestThe task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking accurately and learning to rank are crucial tasks. Context-Based Information Retrieval systems have been of great importance to reduce the effort of finding relevant data. Such systems have evolved by using machine learning techniques to improve their results, but they are mainly dependent on user feedback. Although information retrieval has been addressed in different works along with classifiers based on Optimum-Path Forest (OPF), these have so far not been applied to the learning to rank task. Therefore, the main contribution of this work is to evaluate classifiers based on Optimum-Path Forest, in such a context. Experiments were performed considering the image retrieval and ranking scenarios, and the performance of OPF-based approaches was compared to the well-known SVM-Rank pairwise technique and a baseline based on distance calculation. The experiments showed competitive results concerning precision and outperformed traditional techniques in terms of computational load.Unesp - Univ. Estadual Paulista School of Sciences BauruUFSCar - Federal University of São Carlos Department of ComputingCenpes Petróleo Brasileiro S.A.Ufba - Federal University of BahiaUnesp - Univ. Estadual Paulista School of Sciences BauruUniversidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)S.A.Universidade Federal da Bahia (UFBA)Ascencao, Nathalia Q. [UNESP]Afonso, Luis C. S.Colombo, DaniloOliveira, LucianoPapa, Joao P. [UNESP]2021-06-25T11:05:47Z2021-06-25T11:05:47Z2020-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IJCNN48605.2020.9207689Proceedings of the International Joint Conference on Neural Networks.http://hdl.handle.net/11449/20806610.1109/IJCNN48605.2020.92076892-s2.0-85093868879Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/208066Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Information Ranking Using Optimum-Path Forest |
title |
Information Ranking Using Optimum-Path Forest |
spellingShingle |
Information Ranking Using Optimum-Path Forest Ascencao, Nathalia Q. [UNESP] |
title_short |
Information Ranking Using Optimum-Path Forest |
title_full |
Information Ranking Using Optimum-Path Forest |
title_fullStr |
Information Ranking Using Optimum-Path Forest |
title_full_unstemmed |
Information Ranking Using Optimum-Path Forest |
title_sort |
Information Ranking Using Optimum-Path Forest |
author |
Ascencao, Nathalia Q. [UNESP] |
author_facet |
Ascencao, Nathalia Q. [UNESP] Afonso, Luis C. S. Colombo, Danilo Oliveira, Luciano Papa, Joao P. [UNESP] |
author_role |
author |
author2 |
Afonso, Luis C. S. Colombo, Danilo Oliveira, Luciano Papa, Joao P. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Carlos (UFSCar) S.A. Universidade Federal da Bahia (UFBA) |
dc.contributor.author.fl_str_mv |
Ascencao, Nathalia Q. [UNESP] Afonso, Luis C. S. Colombo, Danilo Oliveira, Luciano Papa, Joao P. [UNESP] |
description |
The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking accurately and learning to rank are crucial tasks. Context-Based Information Retrieval systems have been of great importance to reduce the effort of finding relevant data. Such systems have evolved by using machine learning techniques to improve their results, but they are mainly dependent on user feedback. Although information retrieval has been addressed in different works along with classifiers based on Optimum-Path Forest (OPF), these have so far not been applied to the learning to rank task. Therefore, the main contribution of this work is to evaluate classifiers based on Optimum-Path Forest, in such a context. Experiments were performed considering the image retrieval and ranking scenarios, and the performance of OPF-based approaches was compared to the well-known SVM-Rank pairwise technique and a baseline based on distance calculation. The experiments showed competitive results concerning precision and outperformed traditional techniques in terms of computational load. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-01 2021-06-25T11:05:47Z 2021-06-25T11:05:47Z |
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/10.1109/IJCNN48605.2020.9207689 Proceedings of the International Joint Conference on Neural Networks. http://hdl.handle.net/11449/208066 10.1109/IJCNN48605.2020.9207689 2-s2.0-85093868879 |
url |
http://dx.doi.org/10.1109/IJCNN48605.2020.9207689 http://hdl.handle.net/11449/208066 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks. 10.1109/IJCNN48605.2020.9207689 2-s2.0-85093868879 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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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1797789486863613952 |