A Deep Ensemble Learning Approach for Imbalanced Data in Bankruptcy Prediction

Skewed data distribution poses many challenges in various domains, including the financial sector. Information about a company's potential bankruptcy is crucial for financial institutions and decision-making managers. As bankruptcy prediction has been a key concern for practitioners and research workers for decades, advancements in machine learning offer numerous methods to address imbalanced data scenarios with promising results. Among these, ensembles and neural networks showed remarkable results regarding prediction effectiveness. In this study, we propose a novel deep ensemble boosting approach to overcome imbalanced scenarios called Boosting TabNet. Achieved results showed promising results, specifically in the case of realworld datasets characterized by significant class imbalance ratios. Boosting TabNet outperformed or achieved results comparable to other approaches in most utilized datasets. The highest achieved score in terms of geometric mean (GM) score was over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6 \%}$</tex>.