| An experimental comparison of classification algorithms for imbalanced credit scoring data sets I Brown, C Mues Expert systems with applications 39 (3), 3446-3453, 2012 | 858 | 2012 |
| Benchmarking regression algorithms for loss given default modeling G Loterman, I Brown, D Martens, C Mues, B Baesens International Journal of Forecasting 28 (1), 161-170, 2012 | 245 | 2012 |
| Exposure at default models with and without the credit conversion factor ENC Tong, C Mues, I Brown, LC Thomas European Journal of Operational Research 252 (3), 910-920, 2016 | 51 | 2016 |
| Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals AC Bueff, M Cytryński, R Calabrese, M Jones, J Roberts, J Moore, I Brown Expert Systems with Applications 202, 117271, 2022 | 20 | 2022 |
| Developing credit risk models using SAS enterprise miner and SAS/STAT: Theory and applications I Brown SAS Institute Inc., 2014 | 13 | 2014 |
| Regression model development for credit card exposure at default (EAD) using SAS/STAT and SAS Enterprise Miner 5.3 I Brown SAS Global Forum, 2011 | 7 | 2011 |
| Basel II compliant credit risk modelling: model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD) ILJ Brown University of Southampton, 2012 | 4 | 2012 |
| An experimental comparison of classification techniques for imbalanced credit scoring data sets using SASÒ Enterprise Miner I Brown Proceedings of SAS Global Forum, 2012 | 4 | 2012 |
| Benchmarking state-of-the-art regression algorithms for loss given default modelling G Loterman, I Brown, D Martens, C Mues, B Baesens Credit Scoring and Credit Control XI, 26-28, 2009 | 4 | 2009 |
| Regression model development for exposure at default (EAD) I Brown, C Mues, LC Thomas Proceedings of the 24th European Conference on Operational Research, 2010 | 1 | 2010 |
| Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers I Brown John Wiley & Sons, 2024 | | 2024 |
Christophe MuesProfessor of Data Science and Information Systems, University of Southampton在 soton.ac.uk 的電子郵件地址已通過驗證
