Multi-class imbalanced enterprise credit evaluation based on asymmetric bagging combined with light gradient boosting machine

Abstract

Most existing research on multi-class imbalanced enterprise credit evaluation modeling has been built on data-level imbalance processing methods and single classifier approaches. Using the one-versus-one (OVO) decomposition and fusion method to dispose multi-class classification, this paper proposes two new credit evaluation ensemble models by combining the asymmetric bagging (AB) and the light gradient boosting machine (LightGBM). Based on a multi-class imbalanced dataset of Chinese enterprises that issue corporate bonds from 2014 through 2020, this study conducts a series of empirical experiments for multi-class imbalanced enterprise credit evaluation. The experimental results demonstrate that our proposed models can significantly outperform the benchmark models, which integrate the OVO and one-versus-all decomposition and fusion method respectively with the random under sampling LightGBM, random over sampling LightGBM, synthetic minority over sampling technique LightGBM and the AB decision tree. In addition, the proposed models can carry out analysis on feature importance, which provides decision-making basis for enterprise stakeholders.