Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador

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....

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Jimbo Santana, Patricia, Lanzarini, Laura Cristina, Bariviera, Aurelio
Formato: Articulo
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Particle swarm optimization
Fuzzy classification rules
Credit risk
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125392
https://www.mdpi.com/2227-9091/8/1/2
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125392
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Particle swarm optimization
Fuzzy classification rules
Credit risk
spellingShingle Ciencias Informáticas
Particle swarm optimization
Fuzzy classification rules
Credit risk
Jimbo Santana, Patricia
Lanzarini, Laura Cristina
Bariviera, Aurelio
Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador
topic_facet Ciencias Informáticas
Particle swarm optimization
Fuzzy classification rules
Credit risk
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.
format Articulo
Articulo
author Jimbo Santana, Patricia
Lanzarini, Laura Cristina
Bariviera, Aurelio
author_facet Jimbo Santana, Patricia
Lanzarini, Laura Cristina
Bariviera, Aurelio
author_sort Jimbo Santana, Patricia
title Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador
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
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/125392
https://www.mdpi.com/2227-9091/8/1/2
work_keys_str_mv AT jimbosantanapatricia variationsofparticleswarmoptimizationforobtainingclassificationrulesappliedtocreditriskinfinancialinstitutionsofecuador
AT lanzarinilauracristina variationsofparticleswarmoptimizationforobtainingclassificationrulesappliedtocreditriskinfinancialinstitutionsofecuador
AT barivieraaurelio variationsofparticleswarmoptimizationforobtainingclassificationrulesappliedtocreditriskinfinancialinstitutionsofecuador
bdutipo_str Repositorios
_version_ 1764820451591192578

Ejemplares similares