GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Scientia Generalis |
Texto Completo: | https://scientiageneralis.com.br/index.php/SG/article/view/473 |
Resumo: | This study addresses the use of genetic algorithms (GAs) in conjunction with Data Mining, to predict whether a customer receiving credit will become delinquent, using the database provided free of charge by researcher Cheng Yeh. The choice of AGs is due to their flexibility and the fact that the data contain noise, insertion errors, variability and patterns are unclear. The quantitative and descriptive methodology was used that will evaluate the performance of the algorithm in relation to other data mining techniques. The results obtained were satisfactory, because the GA can predict with an f-score better than several alternative options, although the performance does not stand out so much in the face of the simplest alternative techniques due to the fact that there are only two possibilities and the proportion between the possibilities is very high. |
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GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERNALGORITMOS GENÉTICOS Y MINERÍA DE DATOS APLICADOS PARA PREDECIR EL PATRÓN DEL CLIENTEALGORITMOS GENÉTICOS E MINERAÇÃO DE DADOS APLICADO NA PREDIÇÃO DA INADIPLÊNCIA DE CLIENTESKDDAlgoritmo genéticoMétricas de avaliação;Mineração de dadosGenetic AlgorithmData miningEvaluation metricsKDDAlgoritmo genéticoMinería de datosMétricas de evaluaciónKDDThis study addresses the use of genetic algorithms (GAs) in conjunction with Data Mining, to predict whether a customer receiving credit will become delinquent, using the database provided free of charge by researcher Cheng Yeh. The choice of AGs is due to their flexibility and the fact that the data contain noise, insertion errors, variability and patterns are unclear. The quantitative and descriptive methodology was used that will evaluate the performance of the algorithm in relation to other data mining techniques. The results obtained were satisfactory, because the GA can predict with an f-score better than several alternative options, although the performance does not stand out so much in the face of the simplest alternative techniques due to the fact that there are only two possibilities and the proportion between the possibilities is very high.This study addresses the use of genetic algorithms (GAs) in conjunction with Data Mining, to predict whether a customer receiving credit will become delinquent, using the database provided free of charge by researcher Cheng Yeh. The choice of AGs is due to their flexibility and the fact that the data contain noise, insertion errors, variability and patterns are unclear. The quantitative and descriptive methodology was used that will evaluate the performance of the algorithm in relation to other data mining techniques. The results obtained were satisfactory, because the GA can predict with an f-score better than several alternative options, although the performance does not stand out so much in the face of the simplest alternative techniques due to the fact that there are only two possibilities and the proportion between the possibilities is very high.Esse estudo aborda o uso dos algoritmos genéticos (AGs) em conjunto com Data Mining, conhecido também como Mineração de Dados, para predizer se um cliente ao receber crédito se tornará inadimplente, usando a base de dados disponibilizada gratuitamente pela pesquisadora Cheng Yeh. A escolha dos AGs se deve por conta da sua flexibilidade e o fato dos dados conterem ruídos, erros de inserção, variabilidade e os padrões não serem claros. Foi utilizado a metodologia quantitativa e descritiva que irá avaliar o desempenho do algoritmo em relação às outras técnicas de mineração de dados. Os resultados obtidos foram satisfatórios, pois o AG consegue predizer com um f-score melhor que várias opções alternativas, apesar de que o desempenho não se destaca tanto frente as técnicas alternativas mais simples devido ao fato de haver somente duas possibilidades e a proporção entre as possibilidades é bem alta.Scientia GeneralisScientia GeneralisScientia Generalis2023-02-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://scientiageneralis.com.br/index.php/SG/article/view/47310.22289/sg.V4N1A427363/v4n1a4Scientia Generalis; v. 4 n. 1 (2023); 36-45Scientia Generalis; Vol. 4 No. 1 (2023); 36-45Scientia Generalis; Vol. 4 Núm. 1 (2023); 36-452675-299927363/v4n1reponame:Scientia Generalisinstname:Publicação independenteinstacron:INDEPporhttps://scientiageneralis.com.br/index.php/SG/article/view/473/372Copyright (c) 2023 João Vitor Souza Germano, Lucas Kazuo Mizo Guti Menezes , Mariângela Ferreira Fuentes Molinahttps://creativecommons.org/licenses/by-sa/4.0info:eu-repo/semantics/openAccessGermano, João Vitor SouzaMenezes , Lucas Kazuo Mizo GutiMolina, Mariângela Ferreira Fuentes2023-08-01T03:32:20Zoai:ojs2.scientiageneralis.com.br:article/473Revistahttps://scientiageneralis.com.br/index.php/SGPRIhttps://scientiageneralis.com.br/index.php/SG/oaieditor@scientiageneralis.com.br2675-29992675-2999opendoar:2023-08-01T03:32:20Scientia Generalis - Publicação independentefalse |
dc.title.none.fl_str_mv |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN ALGORITMOS GENÉTICOS Y MINERÍA DE DATOS APLICADOS PARA PREDECIR EL PATRÓN DEL CLIENTE ALGORITMOS GENÉTICOS E MINERAÇÃO DE DADOS APLICADO NA PREDIÇÃO DA INADIPLÊNCIA DE CLIENTES |
title |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
spellingShingle |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN Germano, João Vitor Souza KDD Algoritmo genético Métricas de avaliação; Mineração de dados Genetic Algorithm Data mining Evaluation metrics KDD Algoritmo genético Minería de datos Métricas de evaluación KDD |
title_short |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
title_full |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
title_fullStr |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
title_full_unstemmed |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
title_sort |
GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN |
author |
Germano, João Vitor Souza |
author_facet |
Germano, João Vitor Souza Menezes , Lucas Kazuo Mizo Guti Molina, Mariângela Ferreira Fuentes |
author_role |
author |
author2 |
Menezes , Lucas Kazuo Mizo Guti Molina, Mariângela Ferreira Fuentes |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Germano, João Vitor Souza Menezes , Lucas Kazuo Mizo Guti Molina, Mariângela Ferreira Fuentes |
dc.subject.por.fl_str_mv |
KDD Algoritmo genético Métricas de avaliação; Mineração de dados Genetic Algorithm Data mining Evaluation metrics KDD Algoritmo genético Minería de datos Métricas de evaluación KDD |
topic |
KDD Algoritmo genético Métricas de avaliação; Mineração de dados Genetic Algorithm Data mining Evaluation metrics KDD Algoritmo genético Minería de datos Métricas de evaluación KDD |
description |
This study addresses the use of genetic algorithms (GAs) in conjunction with Data Mining, to predict whether a customer receiving credit will become delinquent, using the database provided free of charge by researcher Cheng Yeh. The choice of AGs is due to their flexibility and the fact that the data contain noise, insertion errors, variability and patterns are unclear. The quantitative and descriptive methodology was used that will evaluate the performance of the algorithm in relation to other data mining techniques. The results obtained were satisfactory, because the GA can predict with an f-score better than several alternative options, although the performance does not stand out so much in the face of the simplest alternative techniques due to the fact that there are only two possibilities and the proportion between the possibilities is very high. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-23 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://scientiageneralis.com.br/index.php/SG/article/view/473 10.22289/sg.V4N1A4 27363/v4n1a4 |
url |
https://scientiageneralis.com.br/index.php/SG/article/view/473 |
identifier_str_mv |
10.22289/sg.V4N1A4 27363/v4n1a4 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://scientiageneralis.com.br/index.php/SG/article/view/473/372 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Scientia Generalis Scientia Generalis Scientia Generalis |
publisher.none.fl_str_mv |
Scientia Generalis Scientia Generalis Scientia Generalis |
dc.source.none.fl_str_mv |
Scientia Generalis; v. 4 n. 1 (2023); 36-45 Scientia Generalis; Vol. 4 No. 1 (2023); 36-45 Scientia Generalis; Vol. 4 Núm. 1 (2023); 36-45 2675-2999 27363/v4n1 reponame:Scientia Generalis instname:Publicação independente instacron:INDEP |
instname_str |
Publicação independente |
instacron_str |
INDEP |
institution |
INDEP |
reponame_str |
Scientia Generalis |
collection |
Scientia Generalis |
repository.name.fl_str_mv |
Scientia Generalis - Publicação independente |
repository.mail.fl_str_mv |
editor@scientiageneralis.com.br |
_version_ |
1797042486052388864 |