Article: Oversampling and instance selection to predict credit risk Journal: International Journal of Applied Pattern Recognition (IJAPR) 2026 Vol.8 No.2 pp.1 - 22 Abstract: Credit risk prediction is essential for financial institutions. Several studies have shown that oversampling and instance selection can improve credit risk prediction. However, the combination of these methods for credit risk prediction has been less studied. This paper conducts empirical research comparing the performance of the most widely used oversampling and instance selection methods and their combined use through successful supervised classifiers for credit risk prediction. The practical significance of our research is the assessment of the different options of application of oversampling and instance selection (including the combination of both that has not been studied before) in credit risk prediction under the same framework employing the most used standard-public data sets and one new dataset that allowed us to conclude which is the best option for credit risk prediction. Inderscience Publishers - linking academia, business and industry through research

Title: Oversampling and instance selection to predict credit risk

Authors: Erwis Melchor-Pérez; José Fco. Martínez-Trinidad; J. Ariel Carrasco-Ochoa; Agustin Santiago-Alvarado

Addresses: Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico

Abstract: Credit risk prediction is essential for financial institutions. Several studies have shown that oversampling and instance selection can improve credit risk prediction. However, the combination of these methods for credit risk prediction has been less studied. This paper conducts empirical research comparing the performance of the most widely used oversampling and instance selection methods and their combined use through successful supervised classifiers for credit risk prediction. The practical significance of our research is the assessment of the different options of application of oversampling and instance selection (including the combination of both that has not been studied before) in credit risk prediction under the same framework employing the most used standard-public data sets and one new dataset that allowed us to conclude which is the best option for credit risk prediction.

Keywords: credit risk; instance selection; oversampling.

DOI: 10.1504/IJAPR.2026.152563

International Journal of Applied Pattern Recognition, 2026 Vol.8 No.2, pp.1 - 22

Received: 16 Aug 2024
Accepted: 03 Mar 2025
Published online: 27 Mar 2026 *

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Title: Oversampling and instance selection to predict credit risk

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