Variable selection in the prediction of business failure using genetic programming
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Variable selection in the prediction of business failure using genetic programmingFecha
2024-04-08Cita bibliográfica
Á. Beade, M. Rodríguez, y J. Santos, «Variable selection in the prediction of business failure using genetic programming», Knowledge-Based Systems, vol. 289, p. 111529, abr. 2024, doi: 10.1016/j.knosys.2024.111529.
Resumen
This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method.
Palabras clave
Business failure
Dimensionality reduction
Feature selection
Evolutionary computation
Genetic programming
Dimensionality reduction
Feature selection
Evolutionary computation
Genetic programming
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Derechos
Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1872-7409
0950-7051
0950-7051