| 2009 |
|---|
| 10 | EE | David Martens,
Bart Baesens,
Tony Van Gestel:
Decompositional Rule Extraction from Support Vector Machines by Active Learning.
IEEE Trans. Knowl. Data Eng. 21(2): 178-191 (2009) |
| 2008 |
|---|
| 9 | EE | David Martens,
Johan Huysmans,
Rudy Setiono,
Jan Vanthienen,
Bart Baesens:
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring.
Rule Extraction from Support Vector Machines 2008: 33-63 |
| 8 | EE | Olivier Vandecruys,
David Martens,
Bart Baesens,
Christophe Mues,
Manu De Backer,
Raf Haesen:
Mining software repositories for comprehensible software fault prediction models.
Journal of Systems and Software 81(5): 823-839 (2008) |
| 2007 |
|---|
| 7 | EE | Stijn Goedertier,
David Martens,
Bart Baesens,
Raf Haesen,
Jan Vanthienen:
Process Mining as First-Order Classification Learning on Logs with Negative Events.
Business Process Management Workshops 2007: 42-53 |
| 6 | EE | David Martens,
Bart Baesens,
Tony Van Gestel,
Jan Vanthienen:
Comprehensible credit scoring models using rule extraction from support vector machines.
European Journal of Operational Research 183(3): 1466-1476 (2007) |
| 5 | EE | David Martens,
Manu De Backer,
Raf Haesen,
Jan Vanthienen,
Monique Snoeck,
Bart Baesens:
Classification With Ant Colony Optimization.
IEEE Trans. Evolutionary Computation 11(5): 651-665 (2007) |
| 2006 |
|---|
| 4 | EE | David Martens,
Manu De Backer,
Raf Haesen,
Bart Baesens,
Christophe Mues,
Jan Vanthienen:
Ant-Based Approach to the Knowledge Fusion Problem.
ANTS Workshop 2006: 84-95 |
| 3 | EE | Johan Huysmans,
David Martens,
Bart Baesens,
Jan Vanthienen,
Tony Van Gestel:
Country Corruption Analysis with Self Organizing Maps and Support Vector Machines.
WISI 2006: 103-114 |
| 2 | EE | David Martens,
Manu De Backer,
Raf Haesen,
Bart Baesens,
Tom Holvoet:
Ants Constructing Rule-Based Classifiers.
Swarm Intelligence in Data Mining 2006: 21-43 |
| 2005 |
|---|
| 1 | EE | Manu De Backer,
Raf Haesen,
David Martens,
Bart Baesens:
A Stigmergy Based Approach to Data Mining.
Australian Conference on Artificial Intelligence 2005: 975-978 |
