Bart Baesens

Contact details

Bart Baesens

Katholieke Universiteit Leuven $Description: Description: W:\Baesens.jpg$

Faculty of Business and Economics

Department of Decision Sciences and Information Management

Naamsestraat 69

B-3000 Leuven

BELGIUM

Tel. +32 (0)16 32 68 84

Fax. +32 (0)16 32 67 32

Office: 03.112

Myfirstname.Mylastname@econ.kuleuven.be

Twitter: DataMiningApps

Facebook: Data Mining with Bart

Education

Master in Commercial Engineering in Management Informatics (K.U.Leuven) 1993-1998
PhD in Applied Economic Sciences (K.U.Leuven) 2003

Teaching

Beleidsinformatiesystemen (Management Information Systems)
Grondslagen van Database Management (Basic Principles of Database Management)
Basic Programming
Credit Scoring and Data Mining

Research Interests

§ Theoretical

§ Neural Networks

§ Support Vector Machines

§ Rule Extraction

§ White-box modeling

§ Survival Analysis

§ Social Networks

§ Applications

§ Credit Risk Management/Credit Scoring / Risk Management / Basel II

§ Fraud detection

§ Customer Relationship Management (churn prediction, customer lifetime value modeling, ...)

§ Software engineering (fault prediction, effort estimation, ...)

§ Business Process Intelligence

§ Web Intelligence and Web Mining

§ Social networks

Book: Credit Risk Management: Basic Concepts

Publications

Journal publications

[1] Verbeke W., Dejaeger K, Martens D., Hur J., Baesens B., New insights into churn prediction in the telecommunication sector: a profit driven data mining approach, European Journal of Operational Research, forthcoming, 2011.

[2] Setiono R., Baesens B. Mues C., Rule Extraction from Minimal Neural Network for Credit Card Screening, International Journal of Neural Systems, Volume 21, Number 4, pp. 265-276, 2011.

[3] Loterman G., Brown I., Martens D., Mues C., Baesens B., Benchmarking Regression Algorithms for Loss Given Default Modeling, International Journal of Forecasting, forthcoming 2011.

[4] Dejaeger K., Verbeke W., Martens D., Baesens B., Data Mining Techniques for Software Effort Estimation: a Comparative Study, IEEE Transactions on Software Engineering, forthcoming 2011.

[5] Van Gool J., Verbeke W., Sercu, P., Baesens B., Credit Scoring for Microfinance - is it worth it?, International Journal of Finance and Economics, forthcoming, 2011.

[6] Martens D., Vanhoutte C., De Winne S., Baesens B. Sels L., Mues C., Identifying Financially Successful Start-Up Profiles with Data Mining, Expert Systems with Applications, Volume 38, pp. 5794–5800, 2011.

[7] Huysmans J., Dejaeger K., Mues C., Vanthienen J., Baesens B., An Empirical Evaluation of the Comprehensibility of Decision Table, Tree and Rule Based Predictive Models, Decision Support Systems, Volume 51, Issue 1, pp. 141-154, 2011.

[8] Martens D., Fawcett T., Baesens B., Editorial Survey: Swarm Intelligence for Data Mining, Machine Learning, Volume 82, Number 1, pp. 1-42, 2010.

[9] Martens D., Vanthienen J., Verbeke W., Baesens B., Performance of classification models from a user perspective, Decision Support Systems, Special Issue on Recent Advances in Data, Text, and Media Mining & Information Issues in Supply Chain and in Service System Design, Volume 51, Issue 4, pp. 782 - 793,

[10] Verbeke W., Martens D., Mues C., Baesens B., Building customer churn prediction models with advanced rule induction techniques, Expert Systems with Applications, Volume 38, pp. 2354-2364, 2011.

[11] Van Gestel T., Martens D., Baesens B., From Linear to Non-linear Kernel Based Classifiers for Bankruptcy Prediction, Neurocomputing, Volume 73, Number 16-18, pp. 2955-2970, 2010.

[12] Lima E., Mues C., Baesens B., Monitoring and Backtesting Churn Models, Expert Systems with Applications, Volume 38, Number 1, pp. 975-982, 2010.

[13] Goedertier S., De Weerdt J., Martens D., Vanthienen J., Baesens B., Process Discovery in Event Logs: An Application in the Telecom Industry, Applied Soft Computing, Volume 11, Number 2, pp. 1697-1710, 2011.

[14] Vuylsteke A., Wen Z., Poelmans J., Baesens B., Consumers' Search for Information on the Internet: How and Why China Differs from Western Europe, Journal of Interactive Marketing, Volume 24, Number 4, pp. 309-331, 2010.

[15] Van Laere E., Baesens B., The development of a simple and intuitive rating system under Solvency II, Insurance: Mathematics and Economics, Volume 46, Issue 3, pp. 500-510, 2010.

[16] Castermans G., Martens D., Van Gestel T., Hamers B., Baesens B., An Overview and Framework for PD Backtesting and Benchmarking, Journal of the Operational Research Society, Special issue on Consumer Credit Risk Modeling, Volume 61, pp. 359-373, 2010.

[17] Martens D., Van Gestel T., De Backer M., Haesen R., Vanthienen J., Baesens B., Credit Rating Prediction Using Ant Colony Optimization, Journal of the Operational Research Society, Volume 61, pp. 561-573, 2010.

[18] Baesens B., Data Mining: new trends, applications and challenges, Review of Business and Economics, Number 1, pp. 46-61, 2009.

[19] Lima E., Mues C., Baesens B., Domain knowledge integration in data mining using decision tables: case studies in churn prediction, Journal of the Operational Research Society, Volume 60, pp. 1096-1106, 2009.

[20] Goedertier S., Martens D., Vanthienen J., Baesens B., Robust Process Discovery with Artificial Negative Events, Journal of Machine Learning Research, Volume 10, pp. 1305-1340, 2009.

[21] Venkataraman G., Rycyna, K., Rabanser A., Heinze G., Baesens B., Ananthanarayanan V., Paner G.P., Barkan G.A., Flanigan R.C., Wojcik E.V., Morphometric Signature differences exist Within Nuclei of Gleason Pattern 4 Areas In Gleason 7 Prostate Cancer With Differing Primary Grades on Needle Biopsy, Journal of Urology, Volume 181, Number 1, pp. 88-94, 2009.

[22] Baesens B., Mues C., Martens D., Vanthienen J., 50 years of Data Mining and OR: upcoming trends and challenges, Journal of the Operational Research Society, Volume 60, pp. 16-23, 2009.

[23] Glady N., Croux C., Baesens B., Modeling Churn Using Customer Lifetime Value, European Journal of Operational Research, Volume 197, Number 1, pp. 402-411, 2009.

[24] Cumps B., Martens D., De Backer M., Viaene S., Dedene G., Haesen R., Snoeck M., Baesens B., Inferring rules for business/ICT alignment using Ants, Information and Management, Volume 46, Number 2, pp. 116-124, 2009.

[25] Martens D., Baesens B., Van Gestel T., Decompositional Rule Extraction from Support Vector Machines by Active Learning, IEEE Transactions on Knowledge and Data Engineering, Volume 21, Number 1, pp. 178-191, 2009.

[26] Glady N., Croux C., Baesens B., A Modified Pareto/NBD Approach for Predicting Customer Lifetime Value, Expert Systems With Applications, Volume 36, Number 2, pp. 2062-2071, 2009.

[27] Setiono R. Baesens B., Mues C., A note on knowledge discovery using neural networks and its application to credit card screening, European Journal of Operational Research, 2008, Volume 192 (1), pp.326-332, 2009.

[28] Lessmann S., Baesens B., Mues C., Pietsch S., Benchmarking classification models for software defect prediction: A proposed framework and novel findings, IEEE Transactions on Software Engineering, Volume 34, Number 4, pp. 485-496, 2008.

[29] Van Laere E., Baesens B., Thibeault A., Bank capital: a myth resolved, Tijdschrift voor Bank en Financiewezen, Volume 1, 2008.

[30] Martens D., Bruynseels L., Baesens B., Willekens M., Vanthienen J., Predicting Going Concern Opinion with Data Mining, Decision Support Systems, Volume 45, pp. 765–777, 2008.

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

[32] Huysmans J., Setiono R., Baesens B., Vanthienen J., Minerva: sequential covering for rule extraction, IEEE Transactions on Systems, Man and Cybernetics, Part B, Volume 38, Number 2, pp. 299-309, 2008.

[33] Setiono R., Baesens B., Mues C. Recursive Neural Network Rule Extraction for Data with Mixed Attributes, IEEE Transactions on Neural Networks, Volume 19, Number 2, pp. 299-307, 2008.

[34] Van Gestel T., Martens D., Baesens B., Feremans D., Huysmans J., Vanthienen J., Forecasting and Analyzing Insurance Companies’Ratings, International Journal of Forecasting, Volume 23, Number 3, pp. 513-529, 2007.

[35] Huysmans J., Baesens B., Vanthienen J., A New Approach for Measuring Rule Set Consistency, Data and Knowledge Engineering, Volume 63, Number 1, pp. 167-182, 2007

[36] Martens D., De Backer M., Haesen R., Vanthienen J., Snoeck M., Baesens B., Classification with Ant Colony Optimization, IEEE Transactions on Evolutionary Computation, Volume 11, Number 5, pp. 651-665, 2007. This paper was most frequently accessed during October 2007.

[37] Martens D., Baesens B., Van Gestel T., Vanthienen J., Comprehensible Credit Scoring Models Using Rule Extraction From Support Vector Machines, European Journal of Operational Research, Volume 183, pp. 1466-1476, 2007

[38] Hoffmann F., Baesens B., Mues C., Van Gestel T., Vanthienen J., Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms, European Journal of Operational Research, Volume 177, Number 1, pp. 540-555, 2006.

[39] Van Gestel T., Baesens B., Van Dijcke P., Suykens J., Garcia J. and Alderweireld T., Linear and nonlinear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk, Volume 1, Number 4, 2005.

[40] Van Gestel T., Baesens B., Van Dijcke P., Garcia J., Suykens J.A.K., Vanthienen J., A process model to develop an internal rating system: sovereign credit ratings, Decision Support Systems, Volume 42, Number 2, pp. 1131-1151, 2006

[41] Huysmans J., Baesens B., Van Gestel T., Vanthienen J., Using Self Organizing Maps for Credit Scoring, Expert Systems With Applications, Special Issue on Intelligent Information Systems for Financial Engineering, Volume 30, Number 3, pp. 479-487, April 2006.

[42] Van Gestel T., Espinoza M., Baesens B., Suykens J.A.K., Brasseur C., De Moor B., A Bayesian Nonlinear Support Vector Machine Error Correction Model, Journal of Forecasting, Volume 25, pp. 77-100, 2006.

[43] Somol P., Baesens B., Pudil P., Vanthienen J., Filter-versus Wrapper-based Feature Selection for Credit Scoring, International Journal of Intelligent Systems, Volume 20, Number 10, pp. 985-999, 2005

[44] Baesens B., Van Gestel T., Mues C., Vanthienen J., Intelligent Information Systems for Financial Engineering, Expert Systems With Applications, Special Issue on Intelligent Information Systems for Financial Engineering, Volume 30, Number 3, pp. 413-414, April 2006.

[45] Van Gestel T., Baesens B., Suykens J.A.K., Van den Poel D., Baestaens D.-E., Willekens M., Bayesian Kernel Based Classification for Financial Distress Detection, European Journal of Operational Research, Volume 172, Number 3, pp. 979-1003, 2006.

[46] Baesens B., Van Gestel T., Stepanova M., Van den Poel D., Vanthienen J., Neural Network Survival Analysis for Personal Loan Data, Journal of the Operational Research Society, Special Issue on Credit Scoring, Volume 59, Number 9, pp. 1089-1098, 2005. .

[47] Egmont-Petersen M., Feelders A., Baesens B., Confidence Intervals for Probabilistic Network Classifiers, Computational Statistics and Data Analysis, Volume 49, Issue 4, pp. 998-1019, 2005

[48] Mues C., Baesens B., Files C.M., Vanthienen J., Decision Diagrams in Machine Learning: an Empirical Study on Real-Life Credit-Risk Data, Expert Systems with Applications, Volume 27, Issue 2, pp. 257-264, September 2004.

[49] Baesens B., Setiono R., Mues C., Vanthienen J., Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation, Management Science, Volume 49, Number 3, pp. 312-329, March 2003.

[50] Baesens B., Van Gestel T., Viaene S., Stepanova M., Suykens J., Vanthienen J., Benchmarking State of the Art Classification Algorithms for Credit Scoring, Journal of the Operational Research Society, Volume 54, Number 6, pp. 627-635, 2003.

[51] Baesens B., Verstraeten G., Van den Poel D., Egmont-Petersen M., Van Kenhove P., Vanthienen J., Bayesian Network Classifiers for Identifying the Slope of the Customer Lifecycle of Long-Life Customers, European Journal of Operational Research, Volume 156, Number 2, pp. 508-523, 2004.

[52] Van Gestel T., Baesens B., Garcia J., Van Dijcke P., A Support Vector Machine Approach to Credit Scoring, Bank en Financiewezen, Volume 2, pp. 73-82, March 2003.

[53] Van Gestel T., Suykens J., Baesens B., Viaene S., Vanthienen J., Dedene G., De Moor B., Vandewalle J., Benchmarking Least Squares Support Vector Machine Classifiers, Machine Learning, Volume 54, Issue 1, pp. 5-32, January 2004

[54] Hoffmann F., Baesens B., Martens J., Put F., Vanthienen J., Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring, International Journal of Intelligent Systems, Volume 17, Issue 11, pp. 1067-1083, 2002.

[55] Viaene S., Derrig R., Baesens B., Dedene G., A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Fraud Detection, Journal of Risk and Insurance, Special issue on Fraud Detection, Volume 69, Issue 3, pp. 433-443, 2002.

[56] Baesens B., Viaene S., Van den Poel D., Vanthienen J., Dedene G., Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing, European Journal of Operational Research, Volume 138, Number 1, pp. 191-211, 2002.

[57] Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J., Wrapped Input Selection using Multilayer Perceptrons for Repeat-Purchase Modeling in Direct Marketing, International Journal of Intelligent Systems in Accounting, Finance and Management, Volume 10, Number 2, pp. 115-126, 2001.

[58] Viaene S., Baesens B., Van Gestel T., Suykens J.A.K., Van den Poel D., Vanthienen J., De Moor B., Dedene G., Knowledge Discovery in a Direct Marketing Case using Least Squares Support Vector Machines, International Journal of Intelligent Systems, Volume 16, Number 9, pp. 1023-1036, 2001.

Books

[1] Van Gestel T., Baesens B., Credit Risk Management: Basic concepts: financial risk components, rating analysis, models, economic and regulatory capital, Oxford University Press, ISBN 978-0-19-954511-7, 2009.

[2] Baesens B., Developing Intelligent Systems for Credit Scoring Using Machine Learning Techniques, Phd Thesis, K.U.Leuven, 2003.

Dutch Journals

[1] De Weerdt J., Schupp A., Vanderloock A., Baesens B., Datagedreven analyse van bedrijfsprocessen op basis van process mining, Informatie, juli/augustus 2011.

[2] Dejaeger K., Verbeke W., Martens D. Baesens B., Het voorspellen van software-ontwikkelkosten, Informatie, november 2010.

[3] Van Gestel T., Dewyspelaere T., Debliquy O., Baesens B., Modelling Credit Portfolios under Stress, Bank- en Financiewezen, Volume 7, pp. 416-422, 2010.

[4] Vuylsteke A., Poelmans J., Baesens B., Online zoekgedrag van consumenten: China vs West-Europa, Business In-Zicht, December 2009.

[5] Baesens B., De Backer M., Martens D., Business intelligence + process management = business process intelligence, Informatie, 2009.

[6] Verbeke W., Baesens B., Van credit crunch naar ICT crash, of niet?, Data News, Number 1, 2009.

[7] De Backer M., Baesens B., BPMN 2.0: meer dan een naamsverandering?, Informatie, 2009.

[8] Baesens B., De Backer M., Business Intelligence: new trends, Informatie, 2009.

[9] Baesens B., Martens D., ICT uitdagingen in het Basel II tijdperk, Informatie, Maart, 2008.

[10] Baesens B., It’s the data, you stupid!, Data News, forthcoming 2007.

[11] Martens D., De Backer M., Haesen R., Baesens B., Artificiële mieren en hun zoektocht naar kennis: Datamining met AntMiner+, Informatie, Mei, 2006.

[12] Huysmans J., Baesens B., Martens D., Denys K., Vanthienen J., New Trends in Data Mining, Tijdschrift voor Economie en Management, Volume L., September 2005.

[13] Haesen R., Martens D., De Backer M., Baesens B., AntMiner+: Een systeem van kennis-ontginnende mieren, Business IN-zicht, Nummer 20, November 2005.

[14] Van Gestel T., Baesens B., Vanthienen J., De impact van Basel II op ICT: een globaal overzicht, Informatie, 2004.

[15] Baesens B., Het ontwikkelen van intelligente systemen voor krediettoekenning met behulp van machine learning technieken, Beleidsinformatica Tijdschrift, Volume 29, Nummer 2, 2003.

[16] Huysmans J., Baesens, B., Vanthienen J., Web Usage Mining: een praktijkstudie, Beleidsinformatica Tijdschrift, Volume 29, Nummer 2, 2003.

[17] Baesens B., Mues C., Vanthienen J., Knowledge Discovery in Data: naar performante én begrijpelijke modellen van bedrijfsintelligentie, Business IN-zicht, Nummer 12, Maart 2003.

[18] Baesens B., Mues C., Vanthienen J., Knowledge Discovery in Data: van academische denkoefening naar bedrijfsrelevante praktijk, Informatie, pp. 30-35, Februari, 2003.

[19] Souverein M., Baesens B., Viaene S., Vanderbist D., Vanthienen J., Een overzicht van web usage mining en de implicaties voor e-commerce, Beleidsinformatica Tijdschrift, Volume 27, Nummer 2, 2001.

[20] Baesens B., ORDBMS'en: de object-relationele verzoening, Beleidsinformatica Tijdschrift, Volume 24, Nummer 3, 1998.

Contributions to Books

[1] Setiono R., Baesens B., Martens D., Rule extraction from neural networks and support vector machines for credit scoring, Data Mining: Foundations and Intelligent Paradigms, D.E. Holmes, L.C. Jain (Eds.), ISRL 25, Springer, 2011.

[2] Martens D., Baesens B., Building Acceptable Classification Models, Annals of Information Systems, Special issue on Data Mining, Stahlbock, R; Crone, S.F.; Lessmann, S. (Eds.), Springer, 2009.

[3] Martens D., Huysmans J., Setiono R., Vanthienen J., Baesens B., Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring, Rule Extraction from Support Vector Machines, Studies in Computational Intelligence, Volume 80, Springer, pp. 33-63, 2006.

[4] Mues C., Baesens B., Huysmans J., Vanthienen J., Comprehensible Credit-Scoring Knowledge Visualization using Decision Tables and Diagrams, Enterprise Information Systems VI, I. Seruca, I. Cordeiro, S. Hammoudi, J. Filipe (Eds.), Springer, pp. 109-115, 2006.

[5] Martens D., De Backer M., Haesen R., Baesens B., Holvoet T., Ants constructing rule-based classifiers, Swarm Intelligence in Data Mining/Stigmergic Optimization, Ajith Abraham, Crina Grosan, Vitorino Ramos (Eds.), Springer Engineering Book Series, Springer, 2005.

[6] Baesens B., Mues C., Setiono R., De Backer M., Vanthienen J., Building Intelligent Credit Scoring Systems using Decision Tables, Enterprise Information Systems V, Olivier Camp, Joaquim B.L. Filipe, Slimane Hammoudi, Mario G. Piattini (Eds.), Kluwer, 2003.

[7] Viaene S., Baesens B., Dedene G., Vanthienen J., Van den Poel D., Proof Running Two State-of-the-Art Pattern Recognition Techniques in the Field of Direct Marketing, Enterprise Information Systems IV, Piattini M., Filipe J., Braz J. (Eds), Kluwer, 2002.

[8] Hoffmann F., Baesens B., Martens J., Put F., Vanthienen J., Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring, Proceedings of the Fifth International FLINS Conference on Computational Intelligent Systems for Applied Research (FLINS’2002), Ruan D., D’hondt P., Kerre E.E. (Eds), ISBN 981-238-066-3, World Scientific, pp. 355-362, 2002.

[9] Baesens B., Setiono R., Mues C., Viaene S., Vanthienen J., Building Credit-Risk Evaluation Expert Systems using Neural Network Rule Extraction and Decision Tables, New Directions in Software Engineering, Liber Amicorum M. Verhelst, Vandenbulcke J. and Snoeck M. (Eds.), 160 pp., Leuven University Press, 2001.

Conference Publications

[1] Van Laere E., Baesens B., Analyzing Bank Ratings: Key Determinants and Procyclicality, Proceedings of the 24th Annual Australasian Finance and Banking Conference, Sydney, December 14^th - 16^th, 2011.

[2] Moges H. T., Dejaeger K., Lemahieu W., Baesens B., Data Quality for Credit Risk Management: New Insights and Challenges, Proceedings of the International Conference on Information Quality (ICIQ 2011), University of South Australia, Adelaide, Australia, November 18-20, 2011.

[3] Verbeke W., Verbraken T., Martens D., Baesens B., Relational Learning for Customer Churn Prediction: The Complementarity of Networked and Non-Networked Classifiers, Proceedings of the Second conference on the Analysis of Mobile Phone Datasets and Networks, Cambridge (US), October 10-11, 2011.

[4] De Weerdt J., De Backer M., Vanthienen J., Baesens B., A Robust F-measure for Evaluating Discovered Process Models, Proceedings of the IEEE Symposium Series in Computational Intelligence 2011 (SSCI 2011), Paris, France, April 2011, Poster Presentation.

[5] De Weerdt J., De Backer M., Vanthienen J., Baesens B., A critical evaluation study of model-log metrics in process discovery, Proceedings of the 6th International Workshop on Business Process Intelligence (BPI’10), New York, U.S., 2010, forthcoming.

[6] Dejaeger K, Hamers B., Poelmans J., Baesens B., A Novel Approach to the Evaluation and Improvement of Data Quality in the Financial Sector, Proceedings of the International Conference on Information Quality (ICIQ 2010), Little Rock, AR, United States, 2010.

[7] Setiono R., Dejaeger K., Verbeke W., Martens D., Baesens B., Software Effort Prediction using Regression Rule Extraction from Neural Networks, Proceedings of 22th International Conference on Tools with Artificial Intelligence, October 27-29, 2010, Arras, France.

[8] Van Laere E., Baesens B., The Development of a Simple and Intuitive Rating System under Solvency II, Proceedings of the Midwest Finance Association 2010 Conference, Las Vegas, NV, U.S.A., February 24-27, 2010.

[9] 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.

[10] Verbeke W., Baesens B., Martens D., De Backer M., Haesen R., Including Domain Knowledge in Customer Churn Prediction Using AntMiner+, Proceedings of the Ninth Industrial Conference on Data Mining - Workshop DMM 2009, Leipzig, Germany, pp. 10-21, July 2009.

[11] Loterman G., Brown I., Martens C., Mues C., Baesens B., Benchmarking state-of-the-art regression algorithms for loss given default modelling, Proceedings of the Conference on Credit Scoring and Credit Control, Edinburgh, United Kingdom, August 2009.

[12] Martens D., Van Gestel T., Vanden Branden K., Jacobs J., Baesens B., A Practical Framework for Credit Risk Stress Testing, Proceedings of the Conference on Credit Scoring and Credit Control, Edinburgh, United Kingdom, August 2009.

[13] Vanhoutte C., Martens D., De Winne S., Sels L., Baesens B., The initial resource-performance relationship in new ventures: Towards a configurational approach, Proceedings of the Seventh AGSE Conference, Queensland, Australia, February 2-5, 2010. McGraw Hill Australia Honourable Mention for Paper in Entrepreneurship Finance, Profitability & Growth.

[14] Van Laere E., Baesens B., Regulatory and economic capital: theory and practice, evidence from the field, Proceedings of the International Risk Management Conference 2009, Financial instability. A new world framework?, Venice, June 22-24, 2009.

[15] Wessa P., Baesens B., Explorative Data Mining of Constructivist Learning Experiences and Activities with Multiple Dimensions, Proceedings of the International Conference on Computer and Instructional Technologies, Dubai, United Arab Emirates, 2009.

[16] Wessa P., Baesens B., Fraud Detection in Statistics Education based on the Compendium Platform and Reproducible Computing, IEEE Proceedings of the World Congress on Computer Science and Information Engineering, Los Angeles/Anaheim, USA, 2009.

[17] Van Laere E., Baesens B., The development of a simple and intuitive rating system under Solvency II, Proceedings of the International Risk Management Conference (IRMC 2008), Credit and Financial Risk Management: 40 years after the Altman Z-score model, Florence, Italy, June, 2008.

[18] Baesens B., Setiono R., Mues C., Neural Network Rule Extraction and Decision Tables for Software Fault Prediction, Proceedings of the Fourteenth International Conference on Neural Information Processing (ICONIP 2007), Special session on "Innovation in Machine Learning and Data Mining”, Lecture Notes in Computer Science, Springer, Kitakyushu, Japan, 2007.

[19] Goedertier S., Martens D., Baesens B., Haesen R., Vanthienen J., Process Mining as First-Order Classification Learning on Logs with Negative Events, Proceedings of the third Workshop on Business Process Intelligence (BPI 07), Lecture Notes In Computer Science, Springer, Brisbane, Australia, September 25-27, 2007.

[20] Glady N., Baesens B., Croux C., A Modified Pareto/NBD Approach for Predicting Customer Lifetime Value, Proceedings of the Statistics for Data Mining, Learning and Knowledge Extraction (IASC 07) Conference, Aveiro, Portugal, August 30 September 1, 2007.

[21] Glady N., Baesens B., Croux C., A Modified Pareto/NBD Approach for Predicting Customer Lifetime Value, 39e Journées de Statistiques ( JDS 2007), Angers, 2007.

[22] Setiono R., Baesens B., Mues C., Risk Management and Regulatory Compliance: A Data Mining Framework Based on Neural Network Rule Extraction, Proceedings of the International Conference on Information Systems (ICIS 2006), Milwaukee, Wisconsin, pp. 71-85, December 10-13, 2006. Best paper Design track

[23] Huysmans J., Baesens B., Vanthienen J., ITER: an Algorithm for Predictive Regression Rule Extraction, Proceedings of the Eighth International Conference on Data Warehousing and Knowledge Discovery (DAWAK), Lecture Notes In Computer Science 4081, Springer, pp. 270-279, Krakow, Poland, September 4-8, 2006.

[24] Martens D., De Backer M., Haesen R., Baesens B., Mues C., Vanthienen J., Ant-Based Approach to the Knowledge Fusion Problem, Proceedings of the Fifth International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 2006), Lecture Notes In Computer Science, Springer, pp. 85-96, Brussels, Belgium, September 4-7, 2006, forthcoming.

[25] Huysmans J., Martens D., Baesens B., Vanthienen J., Country Corruption Analysis with Self Organizing Maps and Support Vector Machines, Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Workshop on Intelligence and Security Informatics (WISI), Lecture Notes in Computer Science, volume 3917, pp. 103-114, Springer-Verlag, Singapore, April 9, 2006.

[26] Van Gestel T., Suykens J.A.K., Pelckmans K., Baesens B., Credit Rating Systems by Combining Linear Ordinal Logistic Regression and Fixed-Size Least Squares Support Vector Machines, Workshop on Machine Learning in Finance, NIPS 2005 Conference, Whistler, British Columbia, Canada, December 9, 2005.

[27] De Backer M., Haesen R., Martens D., Baesens B., A Stigmergy Based Approach to Data Mining, Proceedings of the Eighteenth Australian Joint Conference on Artificial Intelligence (AI 2005), Lecture Notes in Computer Science, Springer-Verlag, pp. 975 – 978, Sydney, Australia, December 5-9, 2005.

[28] Martens D., Baesens B., Van Gestel T., Vanthienen J., Adding Comprehensibility to Support Vector Machine Models Using Rule Extraction Techniques, Proceedings of the Ninth Credit Scoring and Credit Control Conference (CSCCIX’'2005), Edinburgh, United Kingdom, September 2005.

[29] Meeus N., Huysmans J., Baesens B., Vanthienen J., Vandebroek M., .The use of Knowledge Discovery Techniques for Behavioural Scoring, Proceedings of the Sixth International Conference on Data Mining, Text Mining and their Business Applications, May 25-27, Skiathos, Greece, 2005.

[30] Huysmans J., Baesens B., Vanthienen J., A comprehensible SOM-based Scoring System, Proceedings of the International Conference on Machine Learning and Data Mining (MLDM´2005), Lecture Notes in Computer Science, Springer-Verlag, Leipzig, Germany, July 9-11, pp. 80-89, 2005.

[31] Mues C., Baesens B., Vanthienen J., From Knowledge Discovery to Implementation: Developing Business Intelligence Systems using Decision Tables, Proceedings of the Workshop on Knowledge Management and Business Intelligence, Lecture Notes in Computer Science, Springer-Verlag, Kaiserslautern, Germany, April 10-13, pp. 483-495, 2005.

[32] Huysmans J., Baesens B., Vanthienen J., The influence of caching on web usage mining, Proceedings of the Fifth International Conference on Data Mining, Text Mining and their Business Applications, Malaga, Spain, pp.77-86, September 2004.

[33] Huysmans J., Baesens B., Mues C., Vanthienen J., Web usage mining with time constrained association rules, Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, pp. 343-348, April 2004.

[34] Van de Walle P., Callewaert B., Huenaerts C., Molenaers G., Meeusen C., Nijs J., Baesens B., Desloovere K., A Study on Maturation of Oxygen Rate and Cost During Walking and the Influence of Net Non-Dimensional Normalization using Sitting and Standing Data, Proceedings of the Thirteenth Annual ESMAC meeting, Warsaw, Poland, September 23-25, 2004.

[35] Mues C., Baesens B., Huysmans J., Vanthienen J., Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables And Diagrams, Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, pp. 226-232, April 2004.

[36] Huysmans J., Baesens B., Vanthienen J., Web usage mining: a practical study, Proceedings of the Twelfth Conference on Knowledge Acquisition and Management (KAM 2004), Kule, Poland, May 2004.

[37] Mues C., Baesens B., Files C. M., Vanthienen J., Decision diagrams in machine learning: an empirical study on real-life credit-risk data, Proceedings of the Third International Conference on the Theory and Application of Diagrams (Diagrams 2004), Lecture Notes in Computer Science, Springer-Verlag, Cambridge, United Kingdom, March 22-24, 2004.

[38] Baesens B.,Van Gestel T., Stepanova M., Vanthienen J., Neural Network Survival Analysis for Personal Loan Data, Proceedings of the Eighth Conference on Credit Scoring and Credit Control (CSCCVII'2003), Edinburgh, Scotland, September, 2003.

[39] Egmont-Petersen M., Baesens B., Feelders A., Using Bayesian Networks for Estimating the Risk of Default in Credit Scoring, Proceedings of the International Workshop on Computational Management Science, Economics, Finance and Engineering, Limassol, Cyprus, March 2003.

[40] Baesens B., Mues C., Setiono R., De Backer M., Vanthienen J., Building Intelligent Credit Scoring Systems using Decision Tables, Proceedings of the Fifth International Conference on Enterprise Information Systems (ICEIS’2003), Angers, France, pp. 19-25, April 2003. Best paper nomination

[41] Van Gestel T., Baesens B., Suykens J., Espinoza M., Baestaens D.E., Vanthienen J., De Moor B., Bankruptcy Prediction with Least Squares Support Vector Machine Classifiers, Proceedings of the IEEE International Conference on Computational Intelligence for Financial Engineering (CIFEr2003), Hong Kong, pp. 1-8, March 2003.

[42] Buckinx W., Baesens B., Van den Poel D., Van Kenhove P., Vanthienen J., Using Machine Learning Techniques to Predict Defection of Top Clients, Proceedings of the Third International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields, Bologna, Italy, pp. 509-517, September 2002.

[43] Baesens B., Egmont-Petersen M., Castelo R., Vanthienen J., Learning Bayesian Network Classifiers for Credit Scoring using Markov Chain Monte Carlo Search, Proceedings of the Sixteenth International Conference on Pattern Recognition (ICPR'2002), IEEE Computer Society, Québec, Canada, pp. 49-52, August 2002.

[44] Verstraeten G., Baesens B.,Van den Poel D., Egmont-Petersen M., Van Kenhove P., Vanthienen J., Targeting Long-Life Customers: Towards a Segmented CRM Approach, Proceedings of the Thirty-First European Marketing Academy Conference (EMAC’2002), Braga, Portugal, May 2002.

[45] Viaene S., Baesens B., Dedene G., Vanthienen J., Van den Poel D., Proof Running Two State-of-the-Art Pattern Recognition Techniques in the Field of Direct Marketing, Proceedings of the Fourth International Conference on Enterprise Information Systems (ICEIS’2002), Ciudad Real, Spain, pp. 446-454, April 2002. Best paper nomination

[46] Baesens B., Setiono R., Mues C., Viaene S., Vanthienen J., Building Credit-Risk Evaluation Expert Systems using Neural Network Rule Extraction and Decision Tables, Proceedings of the Twenty Second International Conference on Information Systems (ICIS’2001), New Orleans, Louisiana, USA, December, 2001.

[47] Baesens B., Setiono R., De Lille V., Viaene S., Vanthienen J., Neural Network Rule Extraction for Credit Scoring, Proceedings of The Pacific Asian Conference on Intelligent Systems (PAIS’2001), Seoul, Korea, pp. 128-132, November, 2001.

[48] Viaene S., Derrig R., Baesens B., Dedene G., A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Fraud Detection, Proceedings of the Fifth International Congress on Insurance: Mathematics and Economics (IME'2001), Pennsylvania, USA, July, 2001.

[49] Viaene S., Baesens B., Van den Poel D., Vanthienen J., Dedene G., The Bayesian Evidence Framework for Database Marketing Modeling using both RFM and Non-RFM Predictors, Proceedings of the Fifth World Multi-Conference on Systemics, Cybernetics and Informatics (SCI'2001), Orlando, Florida, USA, pp.136-140, July, 2001. Best paper nomination

[50] Baesens B., Viaene S., Vanthienen J., A Comparative Study of State of the Art Classification Algorithms for Credit Scoring, Proceedings of the Seventh Conference on Credit Scoring and Credit Control (CSCCVII'2001), Edinburgh, Scotland, September, 2001.

[51] Baesens B., Viaene S., Van Gestel T., Suykens J.A.K., Dedene G., De Moor B., Vanthienen J., An Initial Approach to Wrapped Input Selection using Least Squares Support Vector Machine Classifiers: Some Empirical Results, Proceedings of the Twelfth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC'00), Kaatsheuvel, The Netherlands, pp. 69-76, November, 2000.

[52] Baesens B., Viaene S., Vanthienen J., Dedene G., Wrapped Feature Selection by means of Guided Neural Network Optimisation, Proceedings of the Fifteenth International Conference on Pattern Recognition (ICPR'2000), IEEE Computer Society, Barcelona, Spain, pp. 113-116, September, 2000.

[53] Viaene S., Baesens B., Van Gestel T., Suykens J.A.K., Van den Poel D., Vanthienen J., De Moor B., Dedene G., Knowledge Discovery using Least Squares Support Vector Machine Classifiers: a Direct Marketing Case, Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2000), D.A. Zighed, J. Komorowski and J. Zytkow (Eds.), Lecture Notes in Artificial Intelligence 1910, Springer, Lyon, France, pp. 657-664, September, 2000. SCI 2005 Impact Factor: 0.402

[54] Baesens B., Viaene S., Van Gestel T., Suykens J.A.K., Dedene G., De Moor B., Vanthienen J., An Empirical assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers, Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES'2000), University of Brighton, UK, pp. 313-316, September, 2000.

[55] Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J., Wrapped Feature Selection for Binary Classification Bayesian Regularisation Neural Networks: a Database Marketing Application, Proceedings of the Second International Conference on DATA MINING 2000, Cambridge University, UK, pp. 353-362, July, 2000.

[56] Baesens B., Viaene S., Vanthienen J., Post-Processing of Association Rules, Proceedings of the special workshop on post-processing, The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2000), Boston, MA, USA, pp. 2-8, August, 2000.

[57] Baesens B., Viaene S., Vanthienen J., Post-Processing of Association Rules, Proceedings of the VIII Seminar on Knowledge Acquisition in Databases, Turawa, Poland, pp.159-173, May, 2000.

[58] Viaene S., Baesens B., Dedene G., Vanthienen J., Vandenbulcke J., Sensitivity Based Pruning of Input Variables by means of Weight Cascaded Retraining, Proceedings of the Fourth International Conference and Exhibition on the Practical Application of Knowledge Discovery and Data Mining (PADD'2000), Manchester, UK, pp.141-159, April, 2000.