Modelo no lineal basado en redes neuronales de unidades producto para clasificación. Una aplicación a la determinación del riesgo en tarjetas de crédito

F. J. Martínez-Estudillo; C. Hervás-Martínez; M. Torres-Jiménez; A. C. Martínez-Estudillo

doi:10.46661/revmetodoscuanteconempresa.2064

Autores/as

F. J. Martínez-Estudillo Departamento de Gestión y Métodos Cuantitativos ETEA Córdoba
C. Hervás-Martínez Departamento de Informática y Análisis Numérico Universidad de Córdoba
M. Torres-Jiménez Departamento de Gestión y Métodos Cuantitativos ETEA Córdoba
A. C. Martínez-Estudillo Departamento de Gestión y Métodos Cuantitativos ETEA Córdoba

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2064

Palabras clave:

Clasificación, redes neuronales de unidades producto, redes neuronales evolutivas, classification, product unit neural networks, evolutionary neural networks

Resumen

El principal objetivo de este trabajo es mostrar un tipo de redes neuronales denominadas redes neuronales basadas en unidades producto (RNUP) como un modelo no lineal que puede ser utilizado para la resolución de problemas de clasificación en aprendizaje. Proponemos un método evolutivo en el que simultáneamente se diseña la estructura de la red y se calculan los correspondientes pesos. La metodología que presentamos se basa, por tanto, en la combinación del modelo no lineal RNUP y del algoritmo evolutivo; se aplica a la resolución de un problema de clasificación de índole económica, surgido del mundo de las finanzas. Para evaluar el rendimiento de los modelos de clasificación obtenidos, comparamos nuestra propuesta con varias técnicas clásicas, como la regresión logística o el análisis discriminante, y con el clásico modelo de perceptrón multicapa de redes neuronales basado en unidades sigmoides y el algoritmo de aprendizaje de retropropagación (MLPBP).

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