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Modelo no lineal basado en redes neuronales de unidades producto para clasificación. Una aplicación a la determinación del riesgo en tarjetas de crédito = Non-linear model for classification based on product-unit neural networks. An application to determine credit card risk


  • Martínez Estudillo, Francisco José

    () (Departamento de Gestión y Métodos Cuantitativos, ETEA Córdoba (España))

  • Hervás Martínez, César

    () (Departamento de Informática y Análisis Numérico, Universidad de Córdoba)

  • Torres Jiménez, Mariano

    () (Departamento de Gestión y Métodos Cuantitativos, ETEA Córdoba (España))

  • Martínez Estudillo, Andrés Carlos

    () (Departamento de Gestión y Métodos Cuantitativos, ETEA Córdoba (España))


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) = The main aim of this work is to show a neural network model called product unit neural network (PUNN), which is a non-linear model to solve classification problems. We propose an evolutionary algorithm to simultaneously design the topology of the network and estimate its corresponding weights. The methodology proposed combines a non-linear model and an evolutionary algorithm and it is applied to solve a real economic problem that occurs in the financial management. To evaluate the performance of the classification models obtained, we compare our approach with several classic statistical techniques such us logistic regression and linear discriminat analysis, and with the multilayer perceptron neural network model based on sigmoidal units trained by means of Back-Propagation algorithm (MLPBP).

Suggested Citation

  • Martínez Estudillo, Francisco José & Hervás Martínez, César & Torres Jiménez, Mariano & Martínez Estudillo, Andrés Carlos, 2007. "Modelo no lineal basado en redes neuronales de unidades producto para clasificación. Una aplicación a la determinación del riesgo en tarjetas de crédito = Non-linear model for classification based on ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 3(1), pages 40-62, June.
  • Handle: RePEc:pab:rmcpee:v:3:y:2007:i:1:p:40-62

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    References listed on IDEAS

    1. David A. Hensher & Stewart Jones, 2007. "Forecasting Corporate Bankruptcy: Optimizing the Performance of the Mixed Logit Model," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 241-264.
    2. Altman, Edward I., 2005. "An emerging market credit scoring system for corporate bonds," Emerging Markets Review, Elsevier, vol. 6(4), pages 311-323, December.
    3. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    4. Felipe Zurita, 2008. "La Predicción de la Insolvencia de Empresas Chilenas," Documentos de Trabajo 336, Instituto de Economia. Pontificia Universidad Católica de Chile..
    5. Tamura, Karin Ayumi & Giampaoli, Viviana, 2013. "New prediction method for the mixed logistic model applied in a marketing problem," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 202-216.
    6. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    7. repec:bla:joares:v:22:y:1984:i::p:59-82 is not listed on IDEAS
    8. Edward I Altman & Tara K N Baidya & Luis Manoel Ribeiro Dias, 1979. "Assessing Potential Financial Problems for Firms in Brazil," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 10(2), pages 9-24, June.
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    More about this item


    clasificación; redes neuronales de unidades producto; redes neuronales evolutivas; classification; product unit neural networks; evolutionary neural networks;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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