David Martens
David Martens
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TitelGeciteerd doorJaar
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
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
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
Editorial survey: swarm intelligence for data mining
D Martens, B Baesens, T Fawcett
Machine Learning 82 (1), 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, 2012
Robust process discovery with artificial negative events
S Goedertier, D Martens, J Vanthienen, B Baesens
Journal of Machine Learning Research 10 (Jun), 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, 2009
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
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
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
Predictive modeling with big data: is bigger really better?
E Junqué de Fortuny, D Martens, F Provost
Big Data 1 (4), 215-226, 2013
Social network analysis for customer churn prediction
W Verbeke, D Martens, B Baesens
Applied Soft Computing 14, 431-446, 2014
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
Explaining data-driven document classifications
D Martens, F Provost
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
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
Ant-based approach to the knowledge fusion problem
D Martens, M De Backer, R Haesen, B Baesens, C Mues, J Vanthienen
International Workshop on Ant Colony Optimization and Swarm Intelligence, 84-95, 2006
Forecasting and analyzing insurance companies' ratings
T Van Gestel, D Martens, B Baesens, D Feremans, J Huysmans, ...
International Journal of Forecasting 23 (3), 513-529, 2007
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