Abstract
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.
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More From: DEStech Transactions on Computer Science and Engineering
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