Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data

Bahareh Amirshahi · Salim Lahmiri

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

Abstract This study explores the performance of gradient boosting methods in bankruptcy prediction for a highly imbalanced dataset. We developed different heterogenous ensemble models based on three popular gradient boosting methods—XGBoost, LightGBM, and CatBoost. Our ensemble models were optimized using the cross‐validation method and the results of the hold‐out test sets showed that the optimized ensemble models not only outperform their base learners, but also improve the state‐of‐the‐art benchmark results on the same dataset. Interestingly, we observed that the data oversampling technique that is commonly used to address the class imbalance issue had an adverse impact on our ensemble models' performance. This indicates that our models are robust to the imbalanced dataset problem that typically degrades the classification performance of machine learning models.

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References

2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction 2021CatBoost model and artificial intelligence techniques for corporate failure prediction 2018A machine learning framework for customer purchase prediction in the non-contractual setting 2018An investigation of bankruptcy prediction in imbalanced datasets 2015Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices 2019Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets 2020CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection 2019Targeting customers for profit: An ensemble learning framework to support marketing decision-making

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