| Benchmarking classification models for software defect prediction: A proposed framework and novel findings S Lessmann, B Baesens, C Mues, S Pietsch IEEE transactions on software engineering 34 (4), 485-496, 2008 | 1410 | 2008 |
| 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 | 765 | 2012 |
| Using neural network rule extraction and decision tables for credit-risk evaluation B Baesens, R Setiono, C Mues, J Vanthienen Management science 49 (3), 312-329, 2003 | 677 | 2003 |
| An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models J Huysmans, K Dejaeger, C Mues, J Vanthienen, B Baesens Decision Support Systems 51 (1), 141-154, 2011 | 452 | 2011 |
| Building comprehensible customer churn prediction models with advanced rule induction techniques W Verbeke, D Martens, C Mues, B Baesens Expert systems with applications 38 (3), 2354-2364, 2011 | 442 | 2011 |
| 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 | 231 | 2012 |
| Recursive neural network rule extraction for data with mixed attributes R Setiono, B Baesens, C Mues IEEE transactions on neural networks 19 (2), 299-307, 2008 | 176 | 2008 |
| Mining software repositories for comprehensible software fault prediction models O Vandecruys, D Martens, B Baesens, C Mues, M De Backer, R Haesen Journal of Systems and software 81 (5), 823-839, 2008 | 167 | 2008 |
| Mixture cure models in credit scoring: If and when borrowers default ENC Tong, C Mues, LC Thomas European Journal of Operational Research 218 (1), 132-139, 2012 | 155 | 2012 |
| Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms F Hoffmann, B Baesens, C Mues, T Van Gestel, J Vanthienen European journal of operational research 177 (1), 540-555, 2007 | 128 | 2007 |
| An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market T Fitzpatrick, C Mues European Journal of Operational Research 249 (2), 427-439, 2016 | 93 | 2016 |
| 50 years of data mining and OR: upcoming trends and challenges B Baesens, C Mues, D Martens, J Vanthienen Journal of the Operational Research Society 60 (sup1), S16-S23, 2009 | 91 | 2009 |
| A zero-adjusted gamma model for mortgage loan loss given default ENC Tong, C Mues, L Thomas International Journal of Forecasting 29 (4), 548-562, 2013 | 90 | 2013 |
| Domain knowledge integration in data mining using decision tables: case studies in churn prediction E Lima, C Mues, B Baesens Journal of the Operational Research Society 60 (8), 1096-1106, 2009 | 90 | 2009 |
| Modelling LGD for unsecured personal loans: Decision tree approach A Matuszyk, C Mues, LC Thomas Journal of the Operational Research Society 61 (3), 393-398, 2010 | 87 | 2010 |
| Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data M Leow, C Mues International Journal of Forecasting 28 (1), 183-195, 2012 | 80 | 2012 |
| Ant-based approach to the knowledge fusion problem D Martens, M De Backer, R Haesen, B Baesens, C Mues, J Vanthienen Ant Colony Optimization and Swarm Intelligence: 5th International Workshop …, 2006 | 64 | 2006 |
| An illustration of verification and validation in the modelling phase of KBS development J Vanthienen, C Mues, A Aerts Data & Knowledge Engineering 27 (3), 337-352, 1998 | 63 | 1998 |
| Rule extraction from minimal neural networks for credit card screening R Setiono, B Baesens, C Mues International journal of neural systems 21 (04), 265-276, 2011 | 54 | 2011 |
| A note on knowledge discovery using neural networks and its application to credit card screening R Setiono, B Baesens, C Mues European Journal of Operational Research 192 (1), 326-332, 2009 | 51 | 2009 |
Bart BaesensFull professor, KU LeuvenVerified email at kuleuven.be
