A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

Abstract

The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).