Evolutionary feature selection approaches for insolvency business prediction with genetic programming

Use this link to cite
http://hdl.handle.net/2183/36888Collections
- Investigación (FEE) [893]
Metadata
Show full item recordTitle
Evolutionary feature selection approaches for insolvency business prediction with genetic programmingDate
2023Citation
Beade, A., Rodríguez López, M. & Santos Reyes, J. (2023). Evolutionary feature selection approaches for insolvency business prediction with genetic programming. Natural computing, 22, 705-722. 10.1007/S11047-023-09951-4
Abstract
[Abstract]: This study uses different feature selection methods in the field of business failure prediction and tests the capability of
Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/noninsolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two
evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method
considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a
simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic
programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used
exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with
GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier
methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction
models.
Keywords
Differential evolution
Genetic programming
Feature selection
Prediction of business insolvency
Genetic programming
Feature selection
Prediction of business insolvency
Editor version
Rights
Atribución 4.0 Internacional
ISSN
1567-7818