Heterogenous Incremental Ensembled Method For Imbalanced Credit Score

In credit risk prediction, assigning credit ratings is crucial. However, it can be challenging while dealing with highly imbalanced data and a varying number of features. The credit rating data is continuously delivered and not provided all at once, which poses a challenge for traditional techniques. To overcome this difficulty, an incremental ensemble model has been introduced for credit scoring, which addresses the issue of class imbalance and the gradual learning process of new data. This approach uses a class balancing technique to tackle class imbalance and allows for the gradual integration of new data. Experimental results have shown that this proposed model outperforms conventional techniques in dealing with uneven credit scoring problems. In summary, the incremental ensemble model provides a solution for handling imbalanced data and gradual learning and it will also deal with the imbalanced data in credit scoring.