David Martens
Cited by
Cited by
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
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
Explaining data-driven document classifications
D Martens, F Provost
MIS Quarterly 38 (1), 73-100, 2014
Editorial survey: swarm intelligence for data mining
D Martens, B Baesens, T Fawcett
Machine Learning 82, 1-42, 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
Predictive Modeling With Big Data: Is Bigger Really Better?
E Junqué de Fortuny, D Martens, F Provost
Big data 1 (4), 215-226, 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
Robust process discovery with artificial negative events
S Goedertier, D Martens, J Vanthienen, B Baesens
Journal of Machine Learning Research 10, 1305-1340, 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
Social network analysis for customer churn prediction
W Verbeke, D Martens, B Baesens
Applied Soft Computing 14, 431-446, 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
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
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
Performance of classification models from a user perspective
D Martens, J Vanthienen, W Verbeke, B Baesens
Decision Support Systems 51 (4), 782-793, 2011
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
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
Bankruptcy prediction for SMEs using relational data
E Tobback, T Bellotti, J Moeyersoms, M Stankova, D Martens
Decision Support Systems 102, 69-81, 2017
Dance Hit Song Prediction
D Herremans, D Martens, K Sörensen
arXiv preprint arXiv:1905.08076, 2019
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