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Researcher
David Martens
- Disciplines:Applied mathematics in specific fields, Artificial intelligence, Information sciences, Information systems, Cognitive science and intelligent systems, Management, Instructional sciences
Affiliations
- Information Systems Engineering Research Group (LIRIS) (main work address Leuven) (Research unit)
Member
From1 Nov 2005 → 30 Sep 2017
Projects
1 - 1 of 1
- Predictive data mining: new techniques and applications.From1 Oct 2008 → 30 Sep 2010Funding: FWO fellowships
Publications
1 - 10 of 35
- RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints(2017)
Authors: Wouter Verbeke, David Martens, Bart Baesens
Pages: 858 - 873 - RULEM: rule learning with monotonicity constraints for ordinal classification(2017)
Authors: Wouter Verbeke, David Martens, Bart Baesens
Pages: 858 - 873 - To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms(2015)
Authors: David Martens, Manu De Backer, Bart Baesens
Pages: 237 - 272 - Comprehensible software fault and effort prediction: a data mining approach(2015)
Authors: Karel Dejaeger, Bart Baesens, David Martens
Pages: 80 - 90 - Forecasting loss given default models: impact of account characteristics and the macroeconomic state(2014)
Authors: David Martens, Bart Baesens
Pages: 376 - 392 - Social network analysis for customer churn prediction(2014)
Authors: Wouter Verbeke, David Martens, Bart Baesens
Pages: 341 - 446 - A novel credit rating migration modeling approach using macroeconomic indicators(2013)
Authors: Wouter Verbeke, David Martens, Bart Baesens
Pages: 654 - 672 - Data mining techniques for software effort estimation: a comparative study(2012)
Authors: Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
Pages: 375 - 397 - Profit driven data mining in massive customer networks: new insights and algorithms.(2012)
Authors: Wouter Verbeke, Bart Baesens, David Martens
- New insights into churn prediction in the telecommunication sector: a profit driven data mining approach(2012)
Authors: Wouter Verbeke, Karel Dejaeger, David Martens, Bart Baesens
Pages: 211 - 229