| 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 | 1371 | 2008 |
| Benchmarking state-of-the-art classification algorithms for credit scoring B Baesens, T Van Gestel, S Viaene, M Stepanova, J Suykens, ... Journal of the operational research society 54, 627-635, 2003 | 1165 | 2003 |
| Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research S Lessmann, B Baesens, HV Seow, LC Thomas European Journal of Operational Research 247 (1), 124-136, 2015 | 917 | 2015 |
| Benchmarking least squares support vector machine classifiers T Van Gestel, JAK Suykens, B Baesens, S Viaene, J Vanthienen, ... Machine learning 54, 5-32, 2004 | 871 | 2004 |
| 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 | 657 | 2003 |
| 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 | 551 | 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 | 541 | 2007 |
| 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 | 492 | 2012 |
| 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 | 436 | 2011 |
| APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions V Van Vlasselaer, C Bravo, O Caelen, T Eliassi-Rad, L Akoglu, M Snoeck, ... Decision Support Systems 75, 38-48, 2015 | 429 | 2015 |
| 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 | 424 | 2011 |
| Credit Risk Management: Basic concepts: Financial risk components, Rating analysis, models, economic and regulatory capital T Van Gestel, B Baesens Oxford University Press, 2009 | 371 | 2009 |
| Analytics in a big data world: The essential guide to data science and its applications B Baesens John Wiley & Sons, 2014 | 324 | 2014 |
| Editorial survey: swarm intelligence for data mining D Martens, B Baesens, T Fawcett Machine learning 82, 1-42, 2011 | 288 | 2011 |
| Transformational issues of big data and analytics in networked business B Baesens, R Bapna, JR Marsden, J Vanthienen, JL Zhao MIS quarterly 40 (4), 807-818, 2016 | 284 | 2016 |
| A comparison of state‐of‐the‐art classification techniques for expert automobile insurance claim fraud detection S Viaene, RA Derrig, B Baesens, G Dedene Journal of Risk and Insurance 69 (3), 373-421, 2002 | 284 | 2002 |
| Modeling churn using customer lifetime value N Glady, B Baesens, C Croux European Journal of Operational Research 197 (1), 402-411, 2009 | 282 | 2009 |
| Bayesian neural network learning for repeat purchase modelling in direct marketing B Baesens, S Viaene, D Van den Poel, J Vanthienen, G Dedene European Journal of Operational Research 138 (1), 191-211, 2002 | 278 | 2002 |
| 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 | 264 | 2011 |
| Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection B Baesens, V Van Vlasselaer, W Verbeke John Wiley & Sons, 2015 | 257 | 2015 |
Jan VanthienenKU LeuvenVerified email at kuleuven.be
