Application and research of ensemble learning in imbalanced credit default prediction

Personal loans are the core and foundation of financial business. Traditional personal credit approval is performed manually. With the large-scale popularization of the Internet, users can conveniently apply for loans through various financial platforms. Personal credit has the characteristics of large crowd size, small amount, short cycle, and high complexity. Manual review is not only inefficient but also has low accuracy in predicting user default. Credit default technology based on machine learning algorithms emerged as the times require. However, the credit default dataset is a typical imbalanced dataset. The imbalanced dataset seriously affects the performance of the algorithm, which causes the algorithm to only focus on majority class samples and ignore minority class samples. This paper proposes an algorithm called multi-granularity class weighting that can effectively handle the class imbalance problem. The main contribution of this paper is to propose an algorithm called multi-granularity class weighting to deal with the class imbalance problem, propose a new feature generation algorithm to generate high-quality features, and use the stacking method in ensemble learning to improve the model performance.