Abstract
Credit scoring is an important area for financial risk management, where a small improvement in prediction performance will save significant losses for financial institutions. This article proposes a two-stage framework for credit scoring based on feature augmentation and dimension reduction to improve the model performance. In this framework, the logarithm marginal density ratios transformation is employed to provide more prominent features for credit scoring, fully utilizing original data information. For the dimension reduction process, we apply three classifiers for penalized logistic regression (add a sum of the parameter absolute value to the criterion function), extreme gradient boosting with regularization term, and k-nearest neighbor with sequential forward selection to two different credit data sets. The results indicate that this two-stage framework can improve the performance of credit scoring models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
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.
