Intelligent model for enhancing the bankruptcy prediction with imbalanced data using oversampling and CatBoost
Joint Authors
Ali, Samar
Alfonse, Marco
Salim, Abd al-Badi M.
Source
International Journal of Intelligent Computing and Information Sciences
Issue
Vol. 22, Issue 3 (31 Aug. 2022), pp.92-108, 17 p.
Publisher
Ain Shams University Faculty of Computer and Information Sciences
Publication Date
2022-08-31
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Bankruptcy prediction is one of the most significant financial decision-making problems, which prevents financial institutions from sever risks.
most of bankruptcy datasets suffer from imbalanced distribution between output classes, which could lead to misclassification in the prediction results.
this research paper presents an efficient bankruptcy prediction model that can handle imbalanced dataset problem by applying synthetic minority oversampling technique (SMOTE) as a pre-processing step.
it applies ensemble-based machine learning classifier, namely, categorical boosting (CatBoost) to classify between active and inactive classes.
moreover, the proposed model reduces the dimensionality of the used dataset to increase predictive performance by using three different feature selection techniques.
the proposed model is evaluated across the most popular imbalanced bankrupt dataset, which is the polish dataset.
the obtained results proved the efficiency of the applied model, especially in terms of the accuracy.
the accuracies of the proposed model in predicting bankruptcy on the Polish five years datasets are 98%, 98%, 97%, 97% and 95%, respectively.
American Psychological Association (APA)
Ali, Samar& Alfonse, Marco& Salim, Abd al-Badi M.. 2022. Intelligent model for enhancing the bankruptcy prediction with imbalanced data using oversampling and CatBoost. International Journal of Intelligent Computing and Information Sciences،Vol. 22, no. 3, pp.92-108.
https://search.emarefa.net/detail/BIM-1495808
Modern Language Association (MLA)
Ali, Samar…[et al.]. Intelligent model for enhancing the bankruptcy prediction with imbalanced data using oversampling and CatBoost. International Journal of Intelligent Computing and Information Sciences Vol. 22, no. 3 (Aug. 2022), pp.92-108.
https://search.emarefa.net/detail/BIM-1495808
American Medical Association (AMA)
Ali, Samar& Alfonse, Marco& Salim, Abd al-Badi M.. Intelligent model for enhancing the bankruptcy prediction with imbalanced data using oversampling and CatBoost. International Journal of Intelligent Computing and Information Sciences. 2022. Vol. 22, no. 3, pp.92-108.
https://search.emarefa.net/detail/BIM-1495808
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 105-108
Record ID
BIM-1495808