GENETIC ALGORITHMS AND DATA MINING APPLIED TO PREDICT CUSTOMER PATTERN

Detalhes bibliográficos
Autor(a) principal: Germano, João Vitor Souza
Data de Publicação: 2023
Outros Autores: Menezes , Lucas Kazuo Mizo Guti, Molina, Mariângela Ferreira Fuentes
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.
id INDEP-1_0012efa761abb750903ef3eed7a95e5c
oai_identifier_str oai:ojs2.scientiageneralis.com.br:article/473
network_acronym_str INDEP-1
network_name_str Scientia Generalis
repository_id_str
spelling 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