About the journal

• Home page
• Contact information
• Aims and scope
• Indexing information
• Editorial policies
• ComSIS consortium
• Journal boards
• Managing board

For authors

• Information for contributors
• Article submission
• Accepted papers
• Copyright transfer form
• Download section

For readers

• Forthcoming articles
• Current issue
• Archive

For reviewers

• Review submissions
• Review submissions OJS

News

• Journal's Facebook page
• Special issue proposal
• Calls for special issues

Using Genetic Programming as a Feature Selector and Classifier to Implement Bankruptcy Prediction Models

Ángel Beade¹, José Santos² and Manuel Rodríguez¹

1. Business Department, University of A Coruña,
   Campus de Elviña, s/n 15071 A Coruña, Spain
   {a.beade, manuel.rodriguez.lopez}@udc.es
2. CITIC (Centre for Information and Communications Technology Research), Department of Computer Science and Information Technologies, University of A Coruña
   Campus de Elviña, s/n 15071 A Coruña, Spain
   jose.santos@udc.es

Abstract

Genetic Programming (GP) was used as a feature selector and classifier to implement bankruptcy prediction models for medium-sized companies. Two sets of input variables were used for the prediction models: one using a large number of exclusively financial variables and the other incorporating variables from the economic environment, which allows analyzing the capability of the latter to improve performance. Two strategies were defined for GP as a feature selector, based on the statistical relevance of the selected features in the GP process, with a novel proposal based on a progressive reduction of the set of selected variables and with the aim of minimizing the risk of eliminating relevant features. An analysis is performed of the improvement obtained with feature selection with both GP-based methods in comparison with the use of complete sets of variables and using GP as a classifier. With the selected variables, we also compared GP as a classifier with respect to other standard classifiers, using automatic parameter adjustment with AutoWeka for these classifiers. The best results are obtained with the synergy of using GP as a feature selector and as a classifier, with the advantage of the direct interpretability that GP provides in the application.

Key words

Genetic programming, feature selection, bankruptcy prediction models

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS250226013B

Publication information

Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
Portable Document Format

How to cite

Beade, Á., Santos, J., Rodríguez, M.: Using Genetic Programming as a Feature Selector and Classifier to Implement Bankruptcy Prediction Models. Computer Science and Information Systems, Vol. 23, No. 1, 513-534. (2026), https://doi.org/10.2298/CSIS250226013B