Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador
- Autores
- Jimbo Santana, Patricia; Lanzarini, Laura Cristina; Bariviera, Aurelio
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.
Instituto de Investigación en Informática - Materia
-
Ciencias Informáticas
Particle swarm optimization
Fuzzy classification rules
Credit risk - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125392
Ver los metadatos del registro completo
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Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of EcuadorJimbo Santana, PatriciaLanzarini, Laura CristinaBariviera, AurelioCiencias InformáticasParticle swarm optimizationFuzzy classification rulesCredit riskKnowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.Instituto de Investigación en Informática2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/125392enginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2227-9091/8/1/2info:eu-repo/semantics/altIdentifier/issn/2227-9091info:eu-repo/semantics/altIdentifier/doi/10.3390/risks8010002info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:08Zoai:sedici.unlp.edu.ar:10915/125392Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:09.015SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
title |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
spellingShingle |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador Jimbo Santana, Patricia Ciencias Informáticas Particle swarm optimization Fuzzy classification rules Credit risk |
title_short |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
title_full |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
title_fullStr |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
title_full_unstemmed |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
title_sort |
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador |
dc.creator.none.fl_str_mv |
Jimbo Santana, Patricia Lanzarini, Laura Cristina Bariviera, Aurelio |
author |
Jimbo Santana, Patricia |
author_facet |
Jimbo Santana, Patricia Lanzarini, Laura Cristina Bariviera, Aurelio |
author_role |
author |
author2 |
Lanzarini, Laura Cristina Bariviera, Aurelio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Particle swarm optimization Fuzzy classification rules Credit risk |
topic |
Ciencias Informáticas Particle swarm optimization Fuzzy classification rules Credit risk |
dc.description.none.fl_txt_mv |
Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision. Instituto de Investigación en Informática |
description |
Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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http://sedici.unlp.edu.ar/handle/10915/125392 |
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http://sedici.unlp.edu.ar/handle/10915/125392 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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