| New insights into churn prediction in the telecommunication sector: A profit driven data mining approach W Verbeke, K Dejaeger, D Martens, J Hur, B Baesens European journal of operational research 218 (1), 211-229, 2012 | 618 | 2012 |
| Comprehensible credit scoring models using rule extraction from support vector machines D Martens, B Baesens, T Van Gestel, J Vanthienen European journal of operational research 183 (3), 1466-1476, 2007 | 595 | 2007 |
| Classification with ant colony optimization D Martens, M De Backer, R Haesen, J Vanthienen, M Snoeck, B Baesens IEEE Transactions on evolutionary computation 11 (5), 651-665, 2007 | 595 | 2007 |
| 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 | 523 | 2011 |
| Explaining data-driven document classifications D Martens, F Provost MIS Quarterly 38 (1), 73-100, 2014 | 385 | 2014 |
| Editorial survey: swarm intelligence for data mining D Martens, B Baesens, T Fawcett Machine Learning 82, 1-42, 2011 | 313 | 2011 |
| Data mining techniques for software effort estimation: a comparative study K Dejaeger, W Verbeke, D Martens, B Baesens IEEE transactions on software engineering 38 (2), 375-397, 2011 | 292 | 2011 |
| Predictive Modeling With Big Data: Is Bigger Really Better? E Junqué de Fortuny, D Martens, F Provost Big data 1 (4), 215-226, 2013 | 284 | 2013 |
| 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 | 261 | 2012 |
| Robust Process Discovery with Artificial Negative Events. S Goedertier, D Martens, J Vanthienen, B Baesens Journal of Machine Learning Research 10 (6), 2009 | 260 | 2009 |
| Decompositional rule extraction from support vector machines by active learning D Martens, BB Baesens, T Van Gestel IEEE Transactions on Knowledge and Data Engineering 21 (2), 178-191, 2008 | 242 | 2008 |
| Social network analysis for customer churn prediction W Verbeke, D Martens, B Baesens Applied Soft Computing 14, 431-446, 2014 | 212 | 2014 |
| Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics D Martens, EJ de Fortuny, J Clark, F Provost MIS Quarterly 40 (4), 2016 | 201 | 2016 |
| Predicting going concern opinion with data mining D Martens, L Bruynseels, B Baesens, M Willekens, J Vanthienen Decision Support Systems 45 (4), 765-777, 2008 | 192 | 2008 |
| Performance of classification models from a user perspective D Martens, J Vanthienen, W Verbeke, B Baesens Decision Support Systems 51 (4), 782-793, 2011 | 178 | 2011 |
| 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 | 177 | 2008 |
| Rule extraction from support vector machines: an overview of issues and application in credit scoring D Martens, J Huysmans, R Setiono, J Vanthienen, B Baesens Rule extraction from support vector machines, 33-63, 2008 | 150 | 2008 |
| Evaluating and understanding text-based stock price prediction models EJ De Fortuny, T De Smedt, D Martens, W Daelemans Information Processing & Management 50 (2), 426-441, 2014 | 144 | 2014 |
| Bankruptcy prediction for SMEs using relational data E Tobback, T Bellotti, J Moeyersoms, M Stankova, D Martens Decision Support Systems 102, 69-81, 2017 | 140 | 2017 |
| A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C Y Ramon, D Martens, F Provost, T Evgeniou Advances in Data Analysis and Classification 14, 801-819, 2020 | 123 | 2020 |
Foster ProvostProfessor, New York UniversityGeverifieerd e-mailadres voor stern.nyu.edu