Intelligent Model for Enhancing the Bankruptcy Prediction with Imbalanced Data Using Oversampling and CatBoost | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Article 20, Volume 22, Issue 3, August 2022, Pages 92-108 PDF (812.74 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2022.105654.1138 | ||
| Authors | ||
| Samar Aly* 1; Marco Alfonse2; Abdel-Badeeh M. Salem3 | ||
| 1Computer Science Department, Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt | ||
| 2Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||
| 3Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University | ||
| Abstract | ||
| 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 ofthe proposed model in predicting bankruptcy on the Polish five years datasets are 98%, 98%, 97%, 97% and 95%, respectively. | ||
| Keywords | ||
| Bankruptcy prediction; Imbalanced dataset; Machine learning; CatBoost; Classification | ||
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