Genetic programming for feature selection in business failure prediction. Comparison of the use of financial variables and economic environment variables

In this work we have experimented with the use of genetic programming as a feature selection method as well as a classifier to obtain business failure prediction models with different prediction temporal horizons. In the prediction models, a wide set of explanatory variables has been used, all of them based on the annual accounts of the company. In addition, an extended set of explanatory variables incorporating variables from the economic environment has been considered. Comparison of the prediction results between these alternatives shows a trend towards better results using the feature selection process, while there is no trend towards better results using economic environment variables.

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Publication Details

Published
2024-09-04
DOI
https://doi.org/10.1109/inista62901.2024.10683824
Citations
1
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.35

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article

Genetic programming for feature selection in business failure prediction. Comparison of the use of financial variables and economic environment variables

Ángel Beade, José Sántos, Manuel Rodríguez
1 citations
Evolutionary Algorithms and Applications
0.35
article

Genetic programming for feature selection in business failure prediction. Comparison of the use of financial variables and economic environment variables

Ángel Beade, José Sántos, Manuel Rodríguez
article en
1 citations

Abstract

In this work we have experimented with the use of genetic programming as a feature selection method as well as a classifier to obtain business failure prediction models with different prediction temporal horizons. In the prediction models, a wide set of explanatory variables has been used, all of them based on the annual accounts of the company. In addition, an extended set of explanatory variables incorporating variables from the economic environment has been considered. Comparison of the prediction results between these alternatives shows a trend towards better results using the feature selection process, while there is no trend towards better results using economic environment variables.

Xunta de Galicia, European Regional Development Fund
Climate action
Openalex Percentile: Top 33%
Evolutionary Algorithms and Applications
0.35
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