Tratamiento de clases desbalanceadas con el método del cubo en problemas de credit scoring a través de la minería de datos

  1. BeltránPascual, Mauricio 1
  2. Francisco Javier Martínez de Pisón Ascacíbar 1
  3. Vicente Virseda, Juan Antonio 2
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Revista:
Cuadernos de economía: Spanish Journal of Economics and Finance

ISSN: 2340-6704,  0210-0266

Año de publicación: 2020

Volumen: 43

Número: 122

Páginas: 175-190

Tipo: Artículo

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Otras publicaciones en: Cuadernos de economía: Spanish Journal of Economics and Finance

Resumen

En este artículo se aborda la forma de aplicar el método de muestreo denominado “Método del cubo” en problemas de credit scoring con la finalidad de poder mejorar la precisión de los modelos predictivos que se obtengan. Este método permite garantizar un óptimo equilibrio de las muestras cuando se trabaja con bases de datos cuyas clases de la variable dependiente están altamente desbalanceadas. Utilizando dos muestras de datos bancarios reales, se realiza un estudio comparativo de los mejores modelos obtenidos con diversos métodos de minería de datos aplicados a las bases de datos originales frente a las balanceadas. Finalmente, se concluye que la capacidad predictiva de los algoritmos de clasificación es más precisa y que los modelos utilizados reducen el coste económico de la clasificación cuando se equilibran las muestras.

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Fuente de los datos: Dialnet