Effect Analysis of Resampling Techniques on the Performance of Customer Credit Scoring Models
Jing Chen, Ling Xie, Dunhu Liu, Jin Xiao · DEStech Transactions on Computer Science and Engineering · 2017
In customer credit scoring, the class distributions of credit scoring datasets are usually imbalanced, which severely affects the performance of credit scoring models. To solve the problem, we introduced 4 evaluation criteria, used 9 classification methods, analyzed the performance of 5 commonly used resampling techniques by extensive experiments on 3 real credit scoring datasets, and performed nonparametric tests on the results. We can conclude that: resampling improves the performance of credit scoring, and the degrees of improvement depend on the evaluation criteria selected; generally, SMOTE performs best; the best resampling technique varies with different models and SMOTE works well with most models. Therefore, the application of resampling techniques should be based on evaluation criteria and models selected.