Paraconsistent random forest : an alternative approach for dealing with uncertain data
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/203970 |
Resumo: | Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy. |
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Favieiro, Gabriela WinklerBalbinot, Alexandre2019-12-28T04:01:39Z20192169-3536http://hdl.handle.net/10183/203970001109095Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy.application/pdfengIEEE Access [recurso eletrônico]. [Piscataway, NJ]. Vol. 7 (2019), p. 147914-147927Árvores de decisõesReconhecimento de padrõesLógica paraconsistenteDecision treesHybrid classifierPattern recognitionParaconsistent logicRandom forestParaconsistent random forest : an alternative approach for dealing with uncertain dataEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001109095.pdf.txt001109095.pdf.txtExtracted Texttext/plain63569http://www.lume.ufrgs.br/bitstream/10183/203970/2/001109095.pdf.txte54d88a271667de008ec2c55fa03ae0bMD52ORIGINAL001109095.pdfTexto completo (inglês)application/pdf9296504http://www.lume.ufrgs.br/bitstream/10183/203970/1/001109095.pdf12a71f9a44bb688e4732b191be1a2d46MD5110183/2039702019-12-29 05:03:20.048049oai:www.lume.ufrgs.br:10183/203970Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2019-12-29T07:03:20Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
title |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
spellingShingle |
Paraconsistent random forest : an alternative approach for dealing with uncertain data Favieiro, Gabriela Winkler Árvores de decisões Reconhecimento de padrões Lógica paraconsistente Decision trees Hybrid classifier Pattern recognition Paraconsistent logic Random forest |
title_short |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
title_full |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
title_fullStr |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
title_full_unstemmed |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
title_sort |
Paraconsistent random forest : an alternative approach for dealing with uncertain data |
author |
Favieiro, Gabriela Winkler |
author_facet |
Favieiro, Gabriela Winkler Balbinot, Alexandre |
author_role |
author |
author2 |
Balbinot, Alexandre |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Favieiro, Gabriela Winkler Balbinot, Alexandre |
dc.subject.por.fl_str_mv |
Árvores de decisões Reconhecimento de padrões Lógica paraconsistente |
topic |
Árvores de decisões Reconhecimento de padrões Lógica paraconsistente Decision trees Hybrid classifier Pattern recognition Paraconsistent logic Random forest |
dc.subject.eng.fl_str_mv |
Decision trees Hybrid classifier Pattern recognition Paraconsistent logic Random forest |
description |
Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-12-28T04:01:39Z |
dc.date.issued.fl_str_mv |
2019 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/203970 |
dc.identifier.issn.pt_BR.fl_str_mv |
2169-3536 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001109095 |
identifier_str_mv |
2169-3536 001109095 |
url |
http://hdl.handle.net/10183/203970 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
IEEE Access [recurso eletrônico]. [Piscataway, NJ]. Vol. 7 (2019), p. 147914-147927 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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