Predicción de incumplimiento de pago de clientes de tarjetas de crédito, con aplicación del algoritmo del k-vecino más cercano y Clas-FriedmanAligned-ST

Luis Niebles-Mamani, Rodrigo Velarde-Herencia, Jose Alfredo Sulla Torres

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Companies that give credit cards to clients face some problems such as non-payment, which is why companies need to control such debts, so as to minimize the risk of recovery of the investment, as a result of debtor clients. In this article, the lazy learning algorithm KNN with the method of statistical evaluation Clas- FriedmanAligned-ST was used, to help us to predict the degree of nonpayment of debts, in order to optimize and improve the prediction performed by data mining algorithms. The database used for this work contains 30000 records, each defined by 25 attributes, of which a significant sample of 5439 instances was taken, with 24 fields. A data processing model is developed, the results are discussed; And concludes with the benefits of evolutionary computing application.

Título traducido de la contribuciónPrediction of non-payment of credit card customers, with application of the k-nearest neighbors algorithm and Clas-FriedmanAligned-ST
Idioma originalEspañol
Título de la publicación alojada15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojadaGlobal Partnership for Development and Engineering Education, LACCEI 2017
EditoresHumberto Alvarez, Maria M. Larrondo Petrie
EditorialLatin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)9780999344309
DOI
EstadoPublicada - 2017
Evento15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017 - Boca Raton, Estados Unidos
Duración: 19 jul. 201721 jul. 2017

Serie de la publicación

NombreProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen2017-July
ISSN (versión digital)2414-6390

Conferencia

Conferencia15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017
País/TerritorioEstados Unidos
CiudadBoca Raton
Período19/07/1721/07/17

Nota bibliográfica

Publisher Copyright:
© 2017 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

  • Credit rating
  • Evolutionary algorithms
  • Lazy learning

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