Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition

Descripción del Articulo

The first and third author acknowledge the Peruvian agencies FONDECYT and CONCYTEC for the support. The second author acknowledges the Brazilian agencies CAPES and FAPESP for the support
Detalles Bibliográficos
Autores: Cardenas, EH, Camargo, HA, Tupac, YJ
Formato: objeto de conferencia
Fecha de Publicación:2016
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/1084
Enlace del recurso:https://hdl.handle.net/20.500.12390/1084
https://doi.org/10.1109/FUZZ-IEEE.2016.7737859
Nivel de acceso:acceso abierto
Materia:pattern classification
fuzzy set theory
genetic algorithms
https://purl.org/pe-repo/ocde/ford#2.02.04
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network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
title Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
spellingShingle Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
Cardenas, EH
pattern classification
fuzzy set theory
genetic algorithms
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
title_full Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
title_fullStr Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
title_full_unstemmed Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
title_sort Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
author Cardenas, EH
author_facet Cardenas, EH
Camargo, HA
Tupac, YJ
author_role author
author2 Camargo, HA
Tupac, YJ
author2_role author
author
dc.contributor.author.fl_str_mv Cardenas, EH
Camargo, HA
Tupac, YJ
dc.subject.none.fl_str_mv pattern classification
topic pattern classification
fuzzy set theory
genetic algorithms
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.es_PE.fl_str_mv fuzzy set theory
genetic algorithms
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description The first and third author acknowledge the Peruvian agencies FONDECYT and CONCYTEC for the support. The second author acknowledges the Brazilian agencies CAPES and FAPESP for the support
publishDate 2016
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/1084
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/FUZZ-IEEE.2016.7737859
dc.identifier.isi.none.fl_str_mv 392150700200
url https://hdl.handle.net/20.500.12390/1084
https://doi.org/10.1109/FUZZ-IEEE.2016.7737859
identifier_str_mv 392150700200
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
publisher.none.fl_str_mv 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp03086600rp03084600rp03085600Cardenas, EHCamargo, HATupac, YJ2024-05-30T23:13:38Z2024-05-30T23:13:38Z2016https://hdl.handle.net/20.500.12390/1084https://doi.org/10.1109/FUZZ-IEEE.2016.7737859392150700200The first and third author acknowledge the Peruvian agencies FONDECYT and CONCYTEC for the support. The second author acknowledges the Brazilian agencies CAPES and FAPESP for the supportIn the last years, multi-objective evolutionary algorithms have been used to learn or tune components of fuzzy systems from data. The suitability of such algorithms for this task is due to the possibility of balancing the conflicting objectives of accuracy and interpretability of the resulting model. In a previous work, a method to learn fuzzy classification rules from imbalanced datasets using multi-objective genetic algorithms and the iterative rule learning approach was proposed by the authors. In this method, the imbalanced datasets are pre-processed to be transformed to balanced datasets, and then the rules are generated and the fuzzy sets are tuned. The method has been evaluated in an experimental study considering ten different methods to pre-process the imbalanced datasets and presented competitive results with comparison to similar proposals. The genetic generation of the rules and the optimization of fuzzy sets were both based on NSGA-II. The objective of this article is to investigate whether the multi-objective algorithm used can impact the performance of the method. In this direction, the work developed here presents and analyses the results obtained by the method proposed before using MOEA/D instead of NSGA-II. The analysis demonstrate that the accuracy obtained with MOEA/D is similar to that of NSGA-II while the interpretability measures such as number of rules and number of conditions tend to be better.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concyteceng2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)info:eu-repo/semantics/openAccesspattern classificationfuzzy set theory-1genetic algorithms-1https://purl.org/pe-repo/ocde/ford#2.02.04-1Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decompositioninfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/1084oai:repositorio.concytec.gob.pe:20.500.12390/10842024-05-30 15:23:40.869http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0f9b2509-ec7e-4845-877c-2fc773729aaa"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <DOI>https://doi.org/10.1109/FUZZ-IEEE.2016.7737859</DOI> <ISI-Number>392150700200</ISI-Number> <Authors> <Author> <DisplayName>Cardenas, EH</DisplayName> <Person id="rp03086" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Camargo, HA</DisplayName> <Person id="rp03084" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Tupac, YJ</DisplayName> <Person id="rp03085" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>pattern classification</Keyword> <Keyword>fuzzy set theory</Keyword> <Keyword>genetic algorithms</Keyword> <Abstract>In the last years, multi-objective evolutionary algorithms have been used to learn or tune components of fuzzy systems from data. The suitability of such algorithms for this task is due to the possibility of balancing the conflicting objectives of accuracy and interpretability of the resulting model. In a previous work, a method to learn fuzzy classification rules from imbalanced datasets using multi-objective genetic algorithms and the iterative rule learning approach was proposed by the authors. In this method, the imbalanced datasets are pre-processed to be transformed to balanced datasets, and then the rules are generated and the fuzzy sets are tuned. The method has been evaluated in an experimental study considering ten different methods to pre-process the imbalanced datasets and presented competitive results with comparison to similar proposals. The genetic generation of the rules and the optimization of fuzzy sets were both based on NSGA-II. The objective of this article is to investigate whether the multi-objective algorithm used can impact the performance of the method. In this direction, the work developed here presents and analyses the results obtained by the method proposed before using MOEA/D instead of NSGA-II. The analysis demonstrate that the accuracy obtained with MOEA/D is similar to that of NSGA-II while the interpretability measures such as number of rules and number of conditions tend to be better.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
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Imbalanced Datasets in the Generation of Fuzzy Classification Systems - An Investigation using a Multiobjective Evolutionary Algorithm based on Decomposition
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