Abstract
For imbalanced credit scoring, the most common solution is to balance the class distribution of the training set with a resampling method, and then train a classification model and classify the customer samples in the test set. However, it is still difficult to select the most appropriate resampling methods and classification models, and the optimal combinations of them have not been identified. Therefore, this study proposes a new benchmark models comparison framework for imbalanced credit scoring. In the framework, we introduce the index of balanced accuracy and four other evaluation measures, experimentally compare the performance of 10 benchmark resampling methods and nine benchmark classification models respectively on six credit scoring data sets, and analyze the optimal combinations of them. The experimental result shows: (1) as for benchmark resampling methods, random under-sampling (a traditional resampling method) and synthetic minority over-sampling technique combined with Wilson’s edited nearest neighbor (an intelligent resampling method) present the best performance; (2) as for benchmark classification models, logistic regression (a single classification model) and adaptive boosting (an ensemble classification model) present the best performance; (3) as for optimal combinations, random under-sampling combined with random subspace (an ensemble classification model) can obtain the most satisfactory credit scoring performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.