Dejaeger K, Verbeke W, Martens D, Baesens B.  2012.  Data Mining Techniques for Software Effort Estimation: a Comparative Study . IEEE Transactions on Software Engineering. 38(2):375-397.

Junqué de Fortuny E, De Smedt T, Martens D, Daelemans W.  2012.  Design and Evaluation of Empirical Models for Stock Price Prediction. Research paper, Faculty of Applied Economics.

Junqué de Fortuny E, De Smedt T, Martens D, Daelemans W.  2012.  Media coverage in times of political crisis: a text mining approach. Expert Systems with Applications. 39:11616–11622.

Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B.  2012.  New insights into churn prediction in the telecommunication sector: a profit driven data mining approach . European Journal of Operational Research. 218(1):211-229.

Minnaert B, Martens D.  2012.  Towards a Particle Swarm Optimization-Based Regression Rule Miner. IEEE 12th International Conference on Data Mining Workshops. :961-963.

Verbeke W, Martens D, Mues C, Baesens B.  2011.  Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications. 38(3):2354-2364.

Martens D, Baesens B, Fawcett T.  2011.  Editorial Survey: Swarm Intelligence for Data Mining . Machine Learning. 82(1):1-42.

Baesens B, Martens D, Setiono R, Zurada J.  2011.  Guest Editorial White Box Nonlinear Prediction Models. IEEE Transactions on Neural Networks. 22(12):2406-2408.

Martens D, Vanhoutte C, Winne S D, Sels L, Baesens B, Mues C.  2011.  Identifying Financially Successful Start-Up Profiles with Data Mining . Expert Systems With Applications. 38:5794-5800.

Martens D, Vanthienen J, Verbeke W, Baesens B.  2011.  Performance of classification models from a user perspective . Decision Support Systems. 51(4):782-793.

Goedertier S, Weerdt J D, Martens D, Vanthienen J, Baesens B.  2011.  Process discovery in event logs: An application in the telecom industry . Applied Soft Computing. 11(2):1697-1710.

Martens D, Provost F.  2011.  Pseudo-social network targeting from consumer transaction data.

Minnaert B, Martens D, backer M D, Baesens B.  2011.  To Tune or not to Tune: Rule Evaluation for Metaheuristic-based Sequential Covering Algorithms. Working Paper, Universiteit Gent.

Martens D, Baesens B.  2010.  Building Acceptable Classification Models . Special Issue on Data Mining. 8:83-74.

Martens D, Gestel T V, backer M D, Haesen R, Vanthienen J, Baesens B.  2010.  Credit Rating Prediction Using Ant Colony Optimization. Journal of the Operational Research Society. 61:561-573.

Gestel T V, Baesens B, Martens D.  2010.  From Linear to Non-linear Kernel Based Classifiers for Bankruptcy Prediction. Neurocomputing. 73(16-18):2955-2970.

Castermans G, Martens D, Gestel T V, Hamers B, Baesens B.  2010.  An Overview and Framework for PD Backtesting and Benchmarking. Journal of the Operational Research Society. 61:359-373.

Baesens B, Mues C, Martens D, Vanthienen J.  2009.  50 years of Data Mining and OR: upcoming trends and challenges . Journal of the Operational Research Society. 60(S1):S16-S23.

Martens D, Van Gestel T, Baesens B.  2009.  Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Transactions on Knowledge and Data Engineering. 21:178-191.

Verbeke W, Baesens B, Martens D, De Backer M, Haesen R.  2009.  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.

Cumps B, Martens D, backer M D, Haesen R, Viaene S, Dedene G, Baesens B, Snoeck M.  2009.  Inferring comprehensible business/ICT alignment rules. Information {&} Management. 46:116-124.

Goedertier S, Martens D, Vanthienen J, Baesens B.  2009.  Robust Process Discovery with Artificial Negative Events. Journal Publications In Press David Martens, Foster Provost Explaining Documents' Classifications MIS Quarterly, SCI 2010 Impact Factor: 5.041 2012 Enric Junqué de Fortuny, Tom De Smedt, David Martens and Walter Daelemans Media coverage in times . 10:1305-1340.

Martens D.  2008.  Building Acceptable Classification Models for Financial Engineering Applications. SIGKDD Explorations. 10:30–31.

Vandecruys O, Martens D, Baesens B, Mues C, De Backer M, Haesen R.  2008.  Mining Software Repositories for Comprehensible Software Fault Prediction Models. Journal of Systems and Software. 81:823-839.

Martens D, Bruynseels L, Baesens B, Willekens M, Vanthienen J.  2008.  Predicting Going Concern Opinion with Data Mining. Decision Support Systems. 45:765-777.