David Martens
David Martens
Verified email at uantwerp.be - Homepage
Title
Cited by
Cited by
Year
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
4692007
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
4442007
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
3262012
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
2882011
Editorial survey: swarm intelligence for data mining
D Martens, B Baesens, T Fawcett
Machine Learning 82 (1), 1-42, 2011
2512011
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
2142011
Robust process discovery with artificial negative events
S Goedertier, D Martens, J Vanthienen, B Baesens
Journal of Machine Learning Research 10, 1305-1340, 2009
1792009
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
1562008
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
1442012
Predictive Modeling With Big Data: Is Bigger Really Better?
E Junqué de Fortuny, D Martens, F Provost
Big Data 1 (4), 215-226, 2013
1392013
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
1332008
Explaining data-driven document classifications
D Martens, F Provost
MIS Quarterly 38 (1), 73-100, 2014
1302014
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
1212008
Social network analysis for customer churn prediction
W Verbeke, D Martens, B Baesens
Applied Soft Computing 14, 431-446, 2014
1172014
Performance of classification models from a user perspective
D Martens, J Vanthienen, W Verbeke, B Baesens
Decision Support Systems 51 (4), 782-793, 2011
972011
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
972008
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics
D Martens, EJ de Fortuny, J Clark, F Provost
MIS Quarterly 40 (4), 2016
942016
Process discovery in event logs: An application in the telecom industry
S Goedertier, J De Weerdt, D Martens, J Vanthienen, B Baesens
Applied Soft Computing 11 (2), 1697-1710, 2011
832011
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
772009
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
642014
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