Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market

Bankruptcy is one of the most critical financial problems that reflects the company's failure. From a machine learning perspective, the problem of bankruptcy prediction is considered a challenging one mainly because of the highly imbalanced distribution of the classes in the datasets. Therefore, developing an efficient prediction model that is able to detect the risky situation of a company is a challenging and complex task. To tackle this problem, in this paper, we propose a hybrid approach that combines the synthetic minority oversampling technique with ensemble methods. Moreover, we apply five different feature selection methods to find out what are the most dominant attributes on bankruptcy prediction. The proposed approach is evaluated based on a real dataset collected from Spanish companies. The conducted experiments show promising results, which prove that the proposed approach can be used as an efficient alternative in case of highly imbalanced datasets.