Bart Baesens
Bart Baesens
Full professor, KU Leuven
Geverifieerd e-mailadres voor kuleuven.be - Homepage
Geciteerd door
Geciteerd door
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
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 (6), 627-635, 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
Benchmarking least squares support vector machine classifiers
T Van Gestel, JAK Suykens, B Baesens, S Viaene, J Vanthienen, ...
Machine learning 54, 5-32, 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
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
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
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
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
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
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
Credit Risk Management: Basic concepts: Financial risk components, Rating analysis, models, economic and regulatory capital
T Van Gestel, B Baesens
Oxford University Press, 2009
Analytics in a big data world: The essential guide to data science and its applications
B Baesens
John Wiley & Sons, 2014
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
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
Modeling churn using customer lifetime value
N Glady, B Baesens, C Croux
European journal of operational research 197 (1), 402-411, 2009
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
Editorial survey: swarm intelligence for data mining
D Martens, B Baesens, T Fawcett
Machine Learning 82, 1-42, 2011
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
A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs
J De Weerdt, M De Backer, J Vanthienen, B Baesens
Information systems 37 (7), 654-676, 2012
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