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Publications

Journal Publications

In Press 

2012

Enric Junqué de Fortuny, Tom De Smedt, David Martens and Walter Daelemans 
Media coverage in times of political crisis: a text mining approach 
Expert Systems With Applications, Vol. 39, Nb. 14, pp. 11616-11622, 2012
 SCI 2010 Impact Factor: 1.926  

Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens 
Data Mining Techniques for Software Effort Estimation: a Comparative Study 
IEEE Transactions on Software Engineering, Vol. 38, Nb. 2, pp. 375-397, 2012
 SCI 2010 Impact Factor: 2.265  

Gert Loterman, Iain Brown, David Martens, Christophe Mues, Bart Baesens
International Journal of Forecasting, Vol. 28, Nb. 1, pp. 161-170, 2012
SCI 2010 Impact Factor: 1.863

2011 2010
2009

David Martens, Bart Baesens, Tony Van Gestel
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Data and Knowledge Engineering, Vol. 21, Nb. 2, pp. 178-191, 2009
SCI 2008 Impact Factor: 2.236

2008

2007

David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens,
Classification with Ant Colony Optimization
IEEE Transactions on Evolutionary Computation Vol. 11, Nb. 5, pp. 651—665, 2007
SCI 2008 Impact Factor: 3.736

Tony Van Gestel, David Martens, Daniel Feremans, Bart Baesens, Johan Huysmans, Jan Vanthienen,
Forecasting and Analyzing Insurance Companies' Ratings
International Journal of Forecasting Vol. 23, Nb. 3, pp. 513—529, 2007
SCI 2008 Impact Factor: 1.685

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 Vol. 183, Nb. 3, pp. 1466—1476, 2007
SCI 2008 Impact Factor: 1.627

2006 2005
    Manu De Backer, Raf Haesen, David Martens, Bart Baesens,
    A Stigmergy Based Approach to Data Mining
    The 18th Australian Joint Conference on Artificial Intelligence (AI 2005), Lecture Notes in Computer Science    , pages 975-978, Sydney (Australia), December 2005
    SCI 2004 Impact Factor: 0.513

    Johan Huysmans, Bart Baesens, David Martens, Katrien Denys, Jan Vanthienen,
    New Trends in Data Mining
    Tijdschrift voor Economie en Management    , Vol. L, September 2005


 

Research Reports

Enric Junqué de Fortuny, Tom De Smedt, David Martens and Walter Daelemans,  Design and Evaluation of Empirical Models for Stock Price Prediction,  University of Antwerp -Faculty of Applied Economics - Research paper 2012-017

Bart Minnaert, David Martens, Manu De Backer, Bart Baesens, To Tune or not to Tune: Rule Evaluation for Metaheuristic-based Sequential Covering Algorithms ,  FEB Working paper 2012/769, University Ghent, January 2012

Enric Junqué de Fortuny, Tom De Smedt, David Martens and Walter Daelemans, Media coverage in times of political crisis: a text mining approach, University of Antwerp - Faculty of Applied Economics - Research paper 2012-003

David Martens, Foster Provost, Pseudo-social network targeting from consumer transaction data , Working paper CeDER-11-05 - New York University - Stern School of Business

David Martens, Foster Provost, Explaining Documents' Classifications, Working paper CeDER-11-01 - New York University - Stern School of Business

Gavin Wims, David Martens, Manu De Backer, Network Models of Financial Contagion: A Definition and Literature Review, Universiteit Gent, Faculteit Economie en Bedrijfskunde - Working paper 11/730, July 2011, 49 p

Stijn Goedertier, David Martens, Bart Baesens, Raf Haesen, Jan Vanthienen, A new approach for discovering business process models from event logs, FETEW Research Report KBI_0716, K.U.Leuven, 20 pp. 2007

Bjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Tijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck, Predicting business/ICT alignment with AntMiner+, FETEW Research Report KBI_0708, K.U.Leuven , 29 pp. 2007

David Martens, Bart Baesens, Tony Van Gestel, Jan Vanthienen, Comprehensible credit scoring models using rule extraction from support vector machines, Research Report 0581, Dept. of Decision Sciences and Information Management, Katholieke Universiteit Leuven, 2005

 


Organizational Activities

Guest editor
- Special Issue on White box non-linear prediction models , for the international journal IEEE Transactions on Neural Networks ( SCI 2008 Impact Factor: 3.726 ), 2010
 - Special Issue on Swarm Intelligence for Knowledge Discovery in Data , for the international journal Machine Learning ( SCI 2008 Impact Factor: 2.326 ), 2010

Session chair
-  Session on Classification in the IEEE 12th International Conference on Data Mining in Brussels, December 2012
 - Session on Data Mining in the 52nd OR conference in London, September 2010
- Session on  Artificial Intelligence and Neural Networks, 21st European Conference on Operational Research,  Reykjavik (Iceland), July 4th 2006

Organizer
-  Industry and Government Track co-chair  of the IEEE 12th International Conference on Data Mining in Brussels, December 2012
- Sponsor co-chair of the IEEE 12th International Conference on Data Mining in Brussels, December 2012
- Workshop organizer on New Frontiers in Data Mining, January 9th 2008

Program Committee Member
- IEEE CIFEr (Computational Intelligence for Financial Engineering & Economics) Conference, New York (United States), March 30, 2012
- The Sixth International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels (Belgium), September 22-24 2008
- The Second International Workshop on Nature Inspired Cooperative Strategies for Optimization, Acireale (Italy), November 8-10 2007
- The Second International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2007), Zhengzhou (China), September 14-17 2007


Publications and Presentations at International Conferences

Active Learning-based Rule Extraction for Regression
Enric Junqué de Fortuny, David Martens
IEEE 12th International Conference on Data Mining Workshops, Brussels (Belgium), pp. 926-933, 2012

Towards a Particle Swarm Optimization-Based Regression Rule Miner
Bart Minnaert, David Martens
IEEE 12th International Conference on Data Mining Workshops, Brussels (Belgium), pp. 961-963, 2012

Geo-Social Targeting for Privacy-friendly Mobile Advertising
Foster Provost, David Martens, Alan Murray
Marketing on the Move: Understanding the Impact of Mobile on Customer Behavior, The Wharton School ,  February 27 - 28, 2012 

Geo-social network advertising
Foster Provost, David Martens, Alan Murray
 2012 Winter Conference on Business Intelligence, Snowbird, Utah, March 1-3, 2012

Geo-Social Targeting for Mobile Advertising. 
Foster Provost, David Martens
The 3rd Workshop on Information in Networks (WIN), New York, NY, September 2011

Relational Learning for Customer Churn Prediction: The Complementarity of Networked and Non-Networked Classifiers
Wouter Verbeke, Thomas Verbraken, David Martens, Bart Baesens
Second conference on the Analysis of Mobile Phone Datasets and Networks , MIT (Media Lab), Cambridge (US), October 10-11, 2011
 
Wouter Verbeke, Karel De Jaegher, Thomas Verbraken, David Martens, Bart Baesens,
Mining social networks for customer churn prediction
Interdisciplinary Workshop on Information and Decision in Social Networks, Cambridge (US), 31 May - 1 June 2011 .

Wouter Verbeke, Karel De Jaegher, David Martens, Bart Baesens,
Benchmarking classification techniques for churn prediciton
2010 Joint Statistical Meeting , July 31 - August 5, 2010, Vancouver, British Columbia

Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
Software Effort Prediction using Regression Rule Extraction from Neural Networks
22th International Conference on Tools with Artificial Intelligence, October 27-29, 2010, Arras, France

Vanhoutte, C., Martens, D., De Winne, S., Sels, L. & Baesens, B,
The initial resource-performance relationship in new ventures: towards a configurational approach.
7th AGSE International Entrepreneurship Research Exchange, University of the Sunshine Coast, Queensland, Australia, February 2-5 2010.
McGraw Hill australia honourable Mention

Verbeke W., Baesens B., Martens D., De Backer M., Haesen R.,
Building Accurate, Comprehensible, and Justifiable Customer Churn Prediction Models using AntMiner+,
Proceedings of the Joint Statistical Meeting, Washington D.C., U.S.A., August 2009.

Verbeke W., Baesens B., Martens D., De Backer M., Haesen R.,
Including Domain Knowledge in Customer Churn Prediction using AntMiner+,
Lecture Notes in Computer Science: Advances in Data Mining, Proceedings of the International Conference on Data Mining, Leipzig, Germany, July 2009.

Applications for Classifications: Key Success Factors and New Domains
KDD 2008 Workshop on Data Mining for Business Applications
Las Vegas (U.S.), August 24th 2008

Active learning for SVM rule extraction
Operational Research Conference 50
York (U.K.), September 10th 2008

An Overview and Framework for PD Backtesting and Benchmarking
Credit Scoring and Credit Control X, 
Edinburgh (U.K.), July 30th 2007

Placing Process Intelligence within the Business Intelligence Framework,
Sixth International Conference on Information and Management Sciences,
Lhasa, Tibet (China), July 3rd 2007

Measuring the Consistency with Prior Knowledge of Classification Models,
Operational Research Conference 48,
Bath (U.K.), September 11 2006

Building Acceptable Classifiers with Ants,
invited speaker in Artificial Intelligence and Neural Networks Stream, 21st European Conference on Operational Research,
Reykjavik (Iceland), July 4th 2006

Benchmarking State-of-the-art Classification Techniques for Credit Scoring,
International Symposium on Applied Stochastic Models and Data Analysis,
Brest (France), May 20th 2005

Adding Comprehensibility to Support Vector Machine Models Using Rule Extraction Techniques
Poster presentation at Credit Scoring and Credit Control IX,
Edinburgh (U.K.), September 8th 2005

On the Use of Ant Systems for Data Mining,
Operational Research Conference 47,
Chester (U.K.), September 15th 2005

 

Papers published as abstracts in conference proceedings

Setiono R, Dejaeger K, Verbeke W, Martens D, Baesens B
Software effort prediction using regression rule extraction from neural networks, International Conference on Tools with Artificial Intelligence (IEEE-ICTAI) (Arras (France)), 2010

Verbeke W, Berteloot K, Castermans G, Martens D, Van Gestel T, Baesens B
Modeling credit rating migrations dependent on the business cycle, EURO 2010 (Lisbon (Portugal)), 2010

David Martens, Tom Fawcett, Bart Baesens
Swarm Intelligence for Data Mining, OR52 Annual Conference, London, UK, September 2010

Christine Vanhoutte, David Martens, Sophie De Winne, Luc Sels, Bart Baesens
The initial resource-performance relationship in new ventures: Towards a configurational approach
Paper presented at the 7th AGSE Conference, February 2-5, Queensland, Australia, 2010

Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen
Incorporating Domain Knowledge in Customer Churn Prediction Using AntMiner+
Joint Statistical Meetings, Section on Statistics and Marketing, Washington, August 2009

Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+
9th Industrial Conference on Data Mining ICDM, Workshop on Data Mining in Marketing DMM, July 22, 2009, Leipzig/Germany

Christine Vanhoutte, David Martens, Luc Sels, Johan Maes, Bart Baesens,
Resource configurations for top performing start-ups: An exploratory study with classification trees
Babson College Entrepreneurship Research Conference (Chapel Hill (USA)), 2008

 

Dutch Publications

Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
De kosten van software ontwikkeling voorspellen, Informatie, Vol. 52, Nb. 9, pp. 8-15, 2010

Bart Baesens, David Martens, Manu De Backer
Business intelligence + process management = business process intelligence, Technieken voor efficiëntere procesanalyse, Informatie, April 2009

David Martens 
Mieren voorspellen de toekomst, IT Professional, pp. 26-27, April 2008

Bart Baesens, David Martens, 
ICT uitdagingen in het Basel II tijdperk, Informatie, pp. 22-25, Maart 2008

David Martens, Manu De Backer, Raf Haesen, Bart Baesens, 
Artificiële mieren en hun zoektocht naar kennis, Informatie, Mei 2006

David Martens, 
Trend: Data Mining, Informatie, December 2005

Manu De Backer, Raf Haesen, David Martens, Bart Baesens, 
Een Systeem van Kennis-Ontginnende Mieren, BusinessInZicht, November 2005


Book Chapters

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, 
Chapter 2 in Book: Rule Extraction from Support Vector Machines, Studies in Computational Intelligence, Vol. 80, Springer, pp. 33-63, 2008

David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet, Ants Building Rule-Based Classifiers, 
Chapter 2 in Book: Swarm Intelligence and Data Mining, Studies in Computational Intelligence , Vol. 34, Springer, pp. 21-44, 2006


Seminar Presentations

Business Applications of New Classification Techniques
Workshop on New Frontiers in Data Mining, 9 January 2008

 Building Acceptable Classification Models for Financial Engineering Applications
2nd doctoral seminar, LIRIS, KULeuven, 17 September 2007

Data Mining with Ant Colony Algorithms: Theory and Applications,
CORMSIS seminar, University of Southampton, 13 February 2007

Building Acceptable Classification Models for Financial Engineering Applications,
1st doctoral seminar, LIRIS, KULeuven, 15 September 2006

Building Rule-Based Classifiers with Ant Colony Optimization,
IRIDIA seminar, ULB Brussels, 24 March 2006


 
Inhoudsverantwoordelijke(n) : david.martens