Ensemble learning algorithms based on easyensemble sampling for financial distress prediction

Abstract

Ensemble learning algorithms show good forecasting performances for financial distress in many studies. Despite considering the feature selection and feature importance procedures, most overlook imbalanced data handling. This study proposes the Easyensemble method based on undersampling and combines it with ensemble learning models to predict financial distress. The results show that Easyensemble sampling presents better forecasting performance than SMOTE sampling. We subsequently conduct Permutation Importance (PIMP), Recursive Feature Elimination (RFE), and partial dependence plots, and the experimental results show that the feature selection procedure can effectively reduce the number of indicators without affecting the prediction accuracy, improve the prediction efficiency as well as save processing time. In addition, the indicators from profitability, cash flow, solvency, and structural ratios are essential in predicting financial distress.