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Journal articles
Lemahieu W., Vanden Broucke S., Baesens B. (2018). An Interview with Bart Baesens, One of the Authors of Principles of Database Management. BIG DATA, 6 (2), 69-71. doi: 10.1089/big.2018.0044.
Nelissen K., Snoeck M., Vanden Broucke S., Baesens B. (2018). Swipe and tell: Using implicit feedback to predict user engagement on tablets. ACM Transactions on Information Systems, 36 (4), doi: 10.1145/3185153.
Baesens B., Verbeke W., Bravo C. (2018). Special Issue on Profit-Driven Analytics. BIG DATA, 6 (1), 1-2. doi: 10.1089/big.2018.29025.bba.
Oskarsdottir M., Baesens B., Vanthienen J. (2018). Profit based model selection for customer retention using individual customer lifetime values. Big Data, 6 (1), 53-65.
Zhu B., Baesens B., Backiel A., Vanden Broucke SK L M. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 69 (1), 49-65. doi: 10.1057/s41274-016-0176-1.
Nelissen K., Snoeck M., Vanden Broucke S., Baesens B. (2018). Swipe and tell: using implicit feedback to predict user engagement on tablets. ACM Transactions on Information Systems, 36 (4 - art. 35).
Oskarsdottir M., Van Calster T., Baesens B., Lemahieu W., Vanthienen J. (2018). Time series for early churn detection: using similarity based classification for dynamic networks. Expert Systems with Applications, 106, 55-65. doi: 10.1016/j.eswa.2018.04.003.
Verbeke W., Martens D., Baesens B. (2017). RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. APPLIED SOFT COMPUTING, 60, 858-873. doi: 10.1016/j.asoc.2017.01.042.
Reusens M., Lemahieu W., Baesens B., Sels L. (2017). A note on explicit versus implicit information for job recommendation. DECISION SUPPORT SYSTEMS, 98, 26-35. doi: 10.1016/j.dss.2017.04.002.
Baesens B., Verbeke W., Bravo C. (2017). Call for Papers: Special Issue on Profit-Driven Analytics. Big Data, 5 (2), 69-70. doi: 10.1089/big.2017.29019.cfp.
Baesens B., Verbeke W., Bravo C. (2017). Call for Papers: Special Issue on Profit-Driven Analytics. Big Data, 5 (1), 3-4. doi: 10.1089/big.2017.29015.cfp.
Zhu B., Baesens B., Backiel A., Vanden Broucke S. (2017). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society 1-17.
Van Vlasselaer V., Eliassi-Rad T., Akoglu L., Snoeck M., Baesens B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63 (9), 3090-3110.
Oskarsdottir M., Bravo C., Verbeke W., Sarraute C., Baesens B., Vanthienen J. (2017). Social network analytics for churn prediction in telco: model building, evaluation and network architecture. Expert Systems with Applications, 85, 204-220.
Van Calster T., Baesens B., Lemahieu W. (2017). ProfARIMA: a profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing, 60, 775-785.
Lismont J., Vanthienen J., Baesens B., Lemahieu W. (2017). Defining analytics maturity indicators: a survey approach. International Journal of Information Management, 37 (3), 114-124. doi: 10.1016/j.ijinfomgt.2016.12.003.
Reusens M., Lemahieu W., Baesens B., Sels L. (2017). A note on explicit versus implicit information for job recommendation. Decision Support Systems, 98, 26-35. doi: 10.1021/acs.jpca.5b00631.
De Winne S., Baesens B., Sels L. (2017). Evidence based HRM en HR analytics: een stand van zaken. Over.werk. Tijdschrift van het Steunpunt Werk, 02, 51-55.
Dirick L., Claeskens G., Baesens B. (2017). Time to default in credit scoring using survival analysis: a benchmark study. Journal of the Operational Research Society, 68 (6), 652-665.
Dirick L., Bellotti T., Claeskens G., Baesens B. (2017). Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models. Journal of Business and Economic Statistics.
Zhu B., Baesens B., Vanden Broucke S. (2017). An empirical comparison of techniques for the class imbalance problem in churn prediction. Information Sciences, 408, 84-99. doi: 10.1016/j.ins.2017.04.015.
Zhu B., Niu Y., Xiao J., Baesens B. (2017). A new transferred feature selection algorithm for customer identification. Neural Computing & Applications, 28 (9), 2593-2603. doi: 10.1007/s00521-016-2214-y.
Mendling J., Baesens B., Bernstein A., Fellmann M. (2017). Challenges of smart business process management: an introduction to the special issue. Decision Support Systems, 100, 1-5.
Li L., Goethals F., Baesens B., Snoeck M. (2017). Predicting software revision outcomes on GitHub using structural holes theory. Computer Networks, 114, 114-124.
Baesens B., De Winne S., Sels L. (2017). Is your company ready for HR analytics?. MIT Sloan Management Review, 58 (2), 20-21.
Baesens B., Bapna R., Marsden JR., Vanthienen J., Zhao JL. (2016). TRANSFORMATIONAL ISSUES OF BIG DATA AND ANALYTICS IN NETWORKED BUSINESS. MIS QUARTERLY, 40 (4), 807-818. doi: 10.25300/MISQ/2016/40:4.03.
Baesens B., De Winne S., Sels L. (2016). What to do before you fire a pivotal employee. Harvard Business Review Art.No. https://hbr.org/2016/01/what-to-do-before-you-fire-a-pivotal-employee.
Backiel A., Baesens B., Claeskens G. (2016). Predicting the time-to-churn of prepaid mobile telephone customers using social network analysis. Journal of the Operational Research Society, 67 (9), 1135-1145.
Moges HT., Van Vlasselaer V., Lemahieu W., Baesens B. (2016). Determining the use of Data Quality Metadata (DQM) for decision making purposes and its impact on decision outcomes. Decision Support Systems, 83, 32-46.
Zhu X., Vanden Broucke S., Zhu G., Vanthienen J., Baesens B. (2016). Enabling flexible location-aware business process modeling and execution. Decision Support Systems, 83, 1-9.
Vanden Broucke S., Caron F., Lismont J., Vanthienen J., Baesens B. (2016). On the gap between reality and registration: a business event analysis classification framework. Information Technology & Management, 17 (4), 393-410.
Lismont J., Vanthienen J., Baesens B., Lemahieu W. (2016). De uitdaging die Big Data heet: pijnpunten en richtlijnen bij integratie van Big Data-analytics. AG Connect, 4, 72-75. (professional oriented).
Baesens B., Bapna R., Marsden J., Vanthienen J., Zhao J. (2016). Transformational issues of big data and analytics in networked business. MIS Quarterly, 40 (4), 1-12.
Maldonado S., Flores A., Verbraken T., Baesens B., Weber R. (2015). Profit-based feature selection using support vector machines - General framework and an application for customer retention. APPLIED SOFT COMPUTING, 35, 740-748. doi: 10.1016/j.asoc.2015.05.058.
Van Vlasselaer V., Bravo C., Caelen O., Eliassi-Rad T., Akoglu L., Snoeck M., Baesens B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48. doi: 10.1016/j.dss.2015.04.013.
Dirick L., Claeskens G., Baesens B. (2015). An Akaike information criterion for multiple event mixture cure models. European Journal of Operational Research, 24, 449-457.
Dirick L., Claeskens G., Baesens B. (2015). Time to default in credit scoring using survival analysis: a benchmark study. FEB Research Report KBI_1522.
Minnaert B., Martens D., De Backer M., Baesens B. (2015). To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms. Data Mining and Knowledge Discovery, 29 (1), 237-272. doi: 10.1007/s10618-013-0339-5.
Seret A., Maldonado S., Baesens B. (2015). Identifying next relevant variables for segmentation by using feature selection approaches. Expert Systems with Applications, 42, 6255-6266.
Lessmann S., Baesens B., Seow HV., Thomas LC. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: A ten-year update. European Journal of Operational Research, 247 (1), 124-136.
Seret A., Bejinaru A., Baesens B. (2015). Domain knowledge based segmentation of online banking customers. Intelligent Data Analysis, 1, 163-184.
Moeyersoms J., Junque de Fortuny E., Dejaeger K., Baesens B., Martens D. (2015). Comprehensible software fault and effort prediction: a data mining approach. The Journal of Systems and Software, 100, 80-90.
Maldonado S., Flores Á., Verbraken T., Weber R., Baesens B. (2015). Profit-based feature selection using support vector machines - general framework and an application for customer retention. Applied Soft Computing, 35, 740-748.
Caron F., Vanthienen J., Vanhaecht K., Van Limbergen E., De Weerdt J., Baesens B. (2015). A process mining based investigation of adverse events in care processes. Health Information Management Journal, 43 (1), 16-25. doi: 10.12826/18333575.2013.0013.Caron.
Dirick L., Claeskens G., Baesens B. (2014). An Akaike information criterion for multiple event mixture cure models. FEB Research Report KBI_1418.
Tobback E., Martens D., Van Gestel T., Baesens B. (2014). Forecasting loss given default models: impact of account characteristics and the macroeconomic state. Journal of the Operational Research Society, 65 (3), 376-392. doi: 10.1057/jors.2013.158.
Ma B., Zhang H., Chen G., Zhao Y., Baesens B. (2014). Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 24 (1), 61-90. doi: 10.1142/S021819401450003X.
Verbraken T., Verbeke W., Baesens B. (2014). Profit optimizing customer churn prediction with Bayesian network classifiers. Intelligent Data Analysis, 18 (1), 3-24.
Seret A., Verbraken T., Baesens B. (2014). A new knowledge-based constrained clustering approach: theory and application in direct marketing. Applied Soft Computing, 24, 316-327. doi: 10.1016/j.asoc.2014.06.002.
Caron F., Vanthienen J., Vanhaecht K., Van Limbergen E., De Weerdt J., Baesens B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44 (1), 88-96.
Caron F., Vanthienen J., Vanhaecht K., Van Limbergen E., Deweerdt J., Baesens B. (2014). A process mining-based investigation of adverse events in care processes. HEALTH INFORMATION MANAGEMENT JOURNAL, 43 (1), 16-25. doi: 10.1177/183335831404300103.
Vanden Broucke S., De Weerdt J., Vanthienen J., Baesens B. (2014). Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering, 26 (8), 1877-1889.
Seret A., Vanden Broucke S., Baesens B., Vanthienen J. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41 (10), 4648-4657. doi: 10.1016/j.eswa.2014.01.022.
Baesens B., Bapna R., Marsden JR., Vanthienen J., Zhao JL. (2014). Transformational issues of big data and analytics in networked business. MIS Quarterly: Management Information Systems, 38 (2), 629-631.
Van Molle E., Vanderloock A., De Weerdt J., Lemahieu W., Sels L., Baesens B., Klewais E., Bouckaert D. (2014). Efficiëntere loopbaanbegeleiding. Informatie 49-53. (professional oriented).
Verbraken T., Bravo C., Weber R., Baesens B. (2014). Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research, 238 (2), 505-513.
Verbraken T., Goethals F., Verbeke W., Baesens B. (2014). Predicting online channel acceptance using social network data. Decision Support Systems, 63, 104-114. doi: 10.1016/j.dss.2013.08.011.
Verbeke W., Martens D., Baesens B. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14, 341-446. doi: 10.1016/j.asoc.2013.09.017.
Caron F., Vanthienen J., Baesens B. (2014). Clinical pathway analytics. Journal of Information Technology Research, 7 (1), 12-26.
Vanden Broucke S., Baesens B., Lismont J., Vanthienen J. (2014). Sluit de lus: moderne technieken in Business Process Analytics. Informatie, 56 (1), 36-45. (professional oriented).
De Weerdt J., Vanden Broucke S., Vanthienen J., Baesens B. (2013). Active Trace Clustering for Improved Process Discovery. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 25 (12), 2708-2720. doi: 10.1109/TKDE.2013.64.
Berteloot K., Verbeke W., Castermans G., Van Gestel T., Martens D., Baesens B. (2013). A novel credit rating migration modeling approach using macroeconomic indicators. Journal of Forecasting, 32 (7), 654-672.
Caron F., Vanthienen J., Baesens B. (2013). Comprehensive rule-based compliance checking and risk management with process mining. DECISION SUPPORT SYSTEMS, 54 (3), 1357-1369. doi: 10.1016/j.dss.2012.12.012.
Caron F., Vanthienen J., Baesens B. (2013). A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Computers in Industry, 64, 464-475. doi: 10.1016/j.compind.2013.02.001.
Verbraken T., Verbeke W., Baesens B. (2013). A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Transactions on Knowledge and Data Engineering, 25 (5), 961-973. doi: 10.1109/TKDE.2012.50.
Dejaeger K., Verbraken T., Baesens B. (2013). Towards comprehensible software fault prediction models using Bayesian network classifiers. IEEE Transactions on Software Engineering, 39 (2), 237-277. doi: 10.1109/TSE.2012.20.
Louis P., Seret A., Baesens B. (2013). Financial efficiency and social impact of microfinance institutions using self-organizing maps. World Development, 45, 197-210. doi: 10.1016/j.worlddev.2013.02.006.
Louis P., Van Laere E., Baesens B. (2013). Understanding and predicting bank rating transitions using optimal survival analysis models. Economics Letters, 119 (3), 280-283. doi: 10.1016/j.econlet.2013.02.033.
De Weerdt J., Schupp A., Vanderloock A., Baesens B. (2013). Process mining for the multi-faceted analysis of business processes - A case study in a financial services organization. Computers in Industry, 64 (1), 57-67. doi: 10.1016/j.compind.2012.09.010.
Moges HT., Dejaeger K., Lemahieu W., Baesens B. (2013). A multidimensional analysis of data quality for credit risk management: new insights and challenges. Information & Management, 50 (1), 43-58.
Louis P., Baesens B. (2013). Do for-profit microfinance institutions achieve better financial efficiency and social impact?. Journal of Development Effectiveness, 5 (3), 359-380. doi: 10.1080/19439342.2013.822015.
Caron F., Vanthienen J., Baesens B. (2013). Comprehensive rule-based compliance checking and risk management with process mining decision support systems. Decision Support Systems, 54 (3), 1357-1369.
Louis P., Seret A., Baesens B. (2013). Financial efficiency and social impact of microfinance institutions using self-organizing maps. Newsletter of the Centre for Financial Services Art.No. 2, 0-0. (professional oriented).
De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2012). A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems, 37 (7), 654-676. doi: 10.1016/j.is.2012.02.004.
Van Gool J., Verbeke W., Sercu P., Baesens B. (2012). Credit scoring for microfinance - Is It worth It?. International Journal of Finance & Economics, 17 (2), 103-123. doi: 10.1002/ijfe.444.
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. doi: 10.1109/TSE.2011.55.
Mehta V., Rycyna K., Baesens B., Barkan GA., Paner GP., Flanigan RC., Wojcik EM., Venkataraman G. (2012). Predictors of Gleason Score (GS) upgrading on subsequent prostatectomy: a single institution study in a cohort of patients with GS 6. International Journal of Clinical and Experimental Pathology, 5 (6), 496-502.
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.
Dejaeger K., Goethals F., Giangreco A., Mola L., Baesens B. (2012). Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 218 (2), Art.No. 562, 548-562. doi: 10.1016/j.ejor.2011.11.022.
Loterman G., Brown I., Martens D., Mues C., Baesens B. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28 (1), 161-170. doi: 10.1016/j.ijforecast.2011.01.006.
Seret A., Verbraken T., Versailles S., Baesens B. (2012). A new SOM-based method for profile generation: theory and an application in direct marketing. European Journal of Operational Research, 220 (1), 199-209.
(2012). Neural networks and learning systems come together. IEEE Trans Neural Netw Learn Syst, 23 (1), 1-6.
Moges HT., Dejaeger K., Lemahieu W., Baesens B. (2012). A total data quality management for credit risk: new insights and challenges. International Journal of Information Quality, 3 (1), 1-27. doi: 10.1504/IJIQ.2012.050036.
Tsujitani M., Baesens B. (2012). Survival analysis for personal loan data using generalized additive models. Behaviormetrika, 39 (1), 1-15.
Baesens B., Martens D., Setiono R., Zurada J. (2011). White box nonlinear prediction models, editorial special issue. IEEE Transactions on Neural Networks, 22 (12), 2406-2408.
Baesens B., Martens D., Setiono R., Zurada JM. (2011). Special Section on White Box Nonlinear Prediction Models. IEEE TRANSACTIONS ON NEURAL NETWORKS, 22 (12), 2406-2408. doi: 10.1109/TNN.2011.2177735.
Martens D., Vanthienen J., Verbeke W., Baesens B. (2011). Performance of classification models from a user perspective. Decision Support Systems, 51 (4), 782-793. doi: 10.1016/j.dss.2011.01.013.
Setiono R., Baesens B., Mues C. (2011). RULE EXTRACTION FROM MINIMAL NEURAL NETWORKS FOR CREDIT CARD SCREENING. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 21 (4), 265-276. doi: 10.1142/S0129065711002821.
Setiono R., Baesens B., Mues C. (2011). Rule extraction from minimal neural network for credit card screening. International Journal of Neural Systems, 21 (4), 265-276.
Huysmans J., Dejaeger K., Mues C., Vanthienen J., Baesens B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51 (1), 141-154. doi: 10.1016/j.dss.2010.12.003.
Goedertier S., De Weerdt J., 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. doi: 10.1016/j.asoc.2010.04.025.
Martens D., Vanhoutte C., De Winne S., Baesens B., Sels L., Mues C. (2011). Identifying financially successful start-up profiles with data mining. Expert Systems with Applications, 38 (5), 5794-5800.
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. doi: 10.1016/j.eswa.2010.08.023.
De Weerdt J., Schupp A., Vanderloock A., Baesens B. (2011). Datagedreven analyseren van bedrijfsprocessen op basis van process mining. Informatie, 53 (6), 34-39. (professional oriented).
Vuylsteke A., Wen Z., Poelmans J., Baesens B. (2010). Consumers' search for information on the internet: how and why China differs from Western Europe. Journal of Interactive Marketing, 24 (4), 309-331. doi: 10.1016/j.intmar.2010.02.010.
Van Laere E., Baesens B. (2010). The development of a simple and intuitive rating system under Solvency II. Insurance: Mathematics & Economics, 46 (3), 500-510. doi: 10.1016/j.insmatheco.2010.01.008.
Baesens B., Martens D., Setiono R., Zurada J. (2010). Special issue of the IEEE transactions on neural networks: White box nonlinear prediction models. IEEE Transactions on Neural Networks, 21 (4), doi: 10.1109/TNN.2010.2046933.
Martens D., Van Gestel T., De Backer M., Haesen R., Vanthienen J., Mues C., Baesens B. (2010). Credit rating prediction using ant colony optimization. Journal of the Operational Research Society, 61 (4), 561-573.
Baesens B., Martens D., Setiono R., Zurada J. (2010). Special issue on white box nonlinear prediction models. IEEE Transactions on Autonomous Mental Development, 2 (1), doi: 10.1109/TAMD.2010.2045438.
Van Gestel T., Dewyspelaere T., Debliquy O., Baesens B. (2010). Modelling credit portfolios under stress. Tijdschrift voor Bank- en Financiewezen, 7, 416-422.
Van Gestel T., Martens D., Baesens B. (2010). From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing, 73 (16), 2955-2970. doi: 10.1016/j.neucom.2010.07.002.
Martens D., Baesens B., Fawcett T. (2010). Editorial survey: swarm intelligence for data mining. Machine Learning, 82 (1), 1-42. doi: 10.1007/s10994-010-5216-5.
Lima E., Mues C., Baesens B. (2010). Monitoring and backtesting churn models. Expert Systems with Applications, 38 (1), 975-982. doi: 10.1016/j.eswa.2010.07.091.
Glady N., Baesens B., Croux C. (2009). Modeling churn using customer lifetime value. European journal of operational research, 197 (1), 402-411.
Lima E., Mues C., Baesens B. (2009). Domain knowledge integration in data mining using decision tables: case studies in churn prediction. Journal of the Operational research society, 60 (8), 1096-1106.
Goedertier S., Martens D., Vanthienen J., Baesens B. (2009). Robust process discovery with artificial negative events. Journal of Machine Learning Research, 10, 1305-1340.
Baesens B., De Backer M. (2009). Business Intelligence: new trends. Informatie 1-10. (professional oriented).
Glady N., Baesens B., Croux C. (2009). A modified Pareto/NBD approach for predicting customer lifetime value. Expert Systems with Applications, 36 (2), 2062-2071.
Martens D., Baesens B., Van Gestel T. (2009). Decompositional rule extraction from support vector machines by active learning. IEEE transactions on knowledge and data engineering, 21 (1), 178-191.
Martens D., Baesens B., Van Gestel T. (2009). Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 21 (2), 178-191. doi: 10.1109/TKDE.2008.131.
Baesens B. (2009). Data mining: new trends, applications and challenges. Review of Business and Economics, 1, 46-61.
Wessa P., Baesens B. (2009). Explorative data mining of constructivist learning experiences and activities with multiple dimensions. Proceedings of the international conference on computer and instructional technologies.
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, 16-23.
Setiono R., Baesens B., Mues C. (2009). A note on knowledge discovery using neural networks and its application to credit card screening. European Journal of Operational Research, 192 (1), 326-332. doi: 10.1016/j.ejor.2007.09.022.
Venkataraman G., Rycyna K., Rabanser A., Heinze G., Baesens B., Ananthanarayanan V., Paner GP., Barkan GA., Flanigan RC., Wojcik EV. (2009). 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, 181 (1), 88-94.
Cumps B., Martens D., De Backer M., Haesen R., Viaene S., Dedene G., Baesens B., Snoeck M. (2009). Inferring comprehensible business/ICT alignment rules. Information & Management, 46 (2), 116-124. doi: 10.1016/j.im.2008.05.005.
Martens D., Bruynseels L., Baesens B., Willekens M., Vanthienen J. (2008). Predicting going concern opinion with data mining. Decision Support Systems, 45 (4), 765-777.
Huysmans J., Baesens B., Vanthienen J. (2008). A data miner's approach to country corruption analysis. Studies in Computational Intelligence, 135, 227-247. doi: 10.1007/978-3-540-69209-6_13.
Lessmann S., Baesens B., Mues C., Pietsch S. (2008). Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE transactions on software engineering, 34 (4), 485-496.
Huysmans J., Setiono R., Baesens B., Vanthienen J. (2008). Minerva: sequential covering for rule extraction. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 38 (2), 299-309.
Baesens B., Martens D. (2008). ICT uitdagingen in het Basel II tijdperk. Informatierecht, 32, 22-25.
Van Laere E., Baesens B., Thibeault A. (2008). Bank capital: a myth resolved. Tijdschrift voor Bank- en Financiewezen, 1, 1-26.
Setiono R., Baesens B., Mues C. (2008). Recursive neural network rule extraction for data with mixed attributes. IEEE Transactions on Neural Networks, 19 (2), 299-307.
Vandecruys O., Martens D., Baesens B., Mues C., De Backer M., Haesen R. (2008). Mining Software Repositories for Comprehensible Software Fault Prediction Models. The Journal of systems and software, 81 (5), 823-839.
Martens D., Baesens B., Van Gestel T., Vanthienen J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European journal of operational research, 183 (3), 1466-1476.
Martens D., De Backer M., Haesen R., Vanthienen J., Snoeck M., Baesens B. (2007). Classification With Ant Colony Optimization. IEEE transactions on evolutionary computation, 11 (5), 651-665.
Huysmans J., Baesens B., Vanthienen J. (2007). A new approach for measuring rule set consistency. Data & knowledge engineering, 63 (1), 167-182.
Glady N., Baesens B., Croux C. (2007). A modified pareto/NBD approach for predicting customer lifetime value.
Hoffmann F., Baesens B., Mues C., Van Gestel T., Vanthienen J. (2007). Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research, 177 (1), 540-555.
Van Gestel T., Martens D., Feremans D., Baesens B., Huysmans J., Vanthienen J. (2007). Forecasting and Analyzing Insurance Companies' Ratings. International journal of forecasting, 23 (3), 513-529.
Van Gestel T., Baesens B., Van Dijcke P., Garcia J., Suykens J., Vanthienen J. (2006). A process model to develop an internal rating system: sovereign credit ratings. Decision Support Systems, 42 (2), 1131-1151.
Van Gestel T., Baesens B., Suykens J., Van den Poel D., Baestaens DE., Willekens M. (2006). Bayesian kernel based classification for financial distress detection. European journal of operational research, 172 (3), 979-1003.
Huysmans J., Baesens B., Vanthienen J., Van Gestel T. (2006). Failure prediction with self organizing maps. Expert systems with applications, 30 (3), 479-487.
Baesens B., Mues C., Van Gestel T., Vanthienen J. (2006). Special issue on intelligent information systems for financial engineering - Preface. Expert systems with applications, 30 (3), 413-414.
Van Gestel T., Espinoza M., Baesens B., Suykens J., Brasseur C., De Moor B. (2006). A Bayesian nonlinear support vector machine error correction model. Journal of Forecasting, 25 (2), 77-100.
Martens D., De Backer M., Haesen R., Baesens B. (2006). Artificiële mieren en hun zoektocht naar kennis. Informatie: Maandblad voor de informatievoorziening, 48 (4), 12-17.
Martens D., De Backer M., Haesen R., Baesens B., Holvoet T., (2006). Ants constructing rule-based classifiers. 21-43.
Baesens B., Van Gestel T., Mues C., Vanthienen J. (2006). Intelligent information systems for financial engineering. Expert Systems with Applications, 30 (3), 413-414.
Huysmans J., Baesens B., Martens D., Denys K., Vanthienen J. (2005). New trends in data mining. Tijdschrift voor Economie en Management (4), 697-711.
Baesens B., Van Gestel T., Stepanova M., Vanthienen J., Van den Poel D. (2005). Neural network survival analysis for personal loan data. Journal of the Operational Research Society, 59 (9), 1089-1098.
Van Gestel T., Baesens B., Van Dijcke P., Suykens J., Garcia J., Alderweireld T. (2005). Linear and nonlinear credit scoring by combining logistic regression and support vector machines. The Journal of Credit Risk, 1 (4).
Somol P., Baesens B., Pudil P., Vanthienen J. (2005). Filter-versus wrapper-based feature selection for credit scoring. International Journal of Intelligent Systems, 20 (10), 985-999.
Baesens B., Verstraeten G., Van den Poel D., Egmont-Petersen M., Van Kenhove P., Vanthienen J. (2004). Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. European journal of operational research, 156 (2), 508-523.
Van Gestel T., Suykens J., Baesens B., Viaene S., Vanthienen J., Dedene G., De Moor B., Vandewalle J. (2004). Benchmarking least squares support vector machine classifiers. Machine learning, 54 (1), 5-32.
Mues C., Baesens B., Files C., Vanthienen J. (2004). Decision diagrams in machine learning: an empirical study on real-life credit-risk data. Expert Systems with Applications, 27 (2), 257-264.
Baesens B., Van Gestel T., Viaene S., Stepanova M., Suykens J., Vanthienen J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the operational research society, 54 (6), 627-635.
Baesens B., Setiono R., Mues C., Vanthienen J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management science, 49 (3), 312-329.
Baesens B., Van Gestel T., Stepanova M., Vanthienen J. (2003). Neural network survival analysis for personal loan data. Proceedings of the Eighth Conference on Credit Scoring and Credit Control (CSCCVII'2003).
Van Gestel T., Baesens B., Garcia J., Van Dijcke P. (2003). A support vector machine approach to credit scoring. Bank- en Financiewezen, 2, 73-82.
Viaene S., Derrig RA., Baesens B., Dedene G. (2002). A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. Journal of risk and insurance, 69 (3), 433-443.
Baesens B., Viaene S., Van den Poel D., Vanthienen J., Dedene G. (2002). Bayesian neural network learning for repeat purchase modelling in direct marketing. European journal of operational research, 138 (1), 191-211.
Van Gestel T., Baesens B., Suykens J., Baesens B., Willekens M., Vanthienen J., De Moor B. (2002). Bayesian kernel based classification for financial distress detection.
Hoffmann F., Baesens B., Martens J., Put F., Vanthienen J. (2002). Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring. International Journal of Intelligent Systems, 17 (11), 1067-1083.
Viaene S., Baesens B., Van Gestel T., Suykens J., Van den Poel D., Vanthienen J., De Moor B., Dedene G. (2001). Knowledge discovery in a direct marketing case using least squares support vector machines. International journal of intelligent systems, 16 (9), 1023-1036.
Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J. (2001). Wrapped feature selection for neural networks in direct marketing. International journal of intelligent systems in accounting, finance & management, 10 (2), 115-126.
Souverein M., Baesens B., Viaene S., Vanderbist D., Vanthienen J. (2001). Een overzicht van web usage mining en de implicaties voor e-commerce. Beleidsinformatica Tijdschrift, 27 (2), 1-26.
Viaene S., Baesens B., Van Gestel T., Suykens J., Van den Poel D., Vanthienen J., De Moor B., Dedene G. (2000). Knowledge discovery using least squares support vector machine classifiers : a direct marketing case. Lecture Notes in Computer Science, 1910, 657-664.
Accepted Journal articles
Baesens B., Vanden Broucke S., Höppner S., Verdonck T., Stripling E. (2018). Profit Driven Decision Trees for Churn Prediction. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH.
Baesens B., Vanthienen J., Oskarsdottir M., Sarraute C., Bravo C. (2018). The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics. APPLIED SOFT COMPUTING.
Zhu Z., Wang X., Baesens B. (2018). On the Optimal Marketing Aggressiveness Level of C2C Sellers in Social Media: Evidence from China. The International Journal of Management Science, accepted.
Lismont J., Cardinaels E., Bruynseels L., De Groote S., Baesens B., Lemahieu W., Vanthienen J. (2018). Predicting tax avoidance by means of social network analytics. Decision Support Systems.
Lismont J., Ram S., Vanthienen J., Lemahieu W., Baesens B. (2018). Predicting interpurchase time in a retail environment using customer-product networks: an empirical study and evaluation. Expert Systems with Applications.
Reusens M., Lemahieu W., Baesens B., Sels L. (2018). Evaluating recommendation and search in the labor market. Knowledge-based Systems.
Mitrovic S., Baesens B., Lemahieu W., De Weerdt J. (2018). On the operational efficiency of different feature types for telco churn prediction. European Journal of Operational Research.
Baesens B., Verbeke W., Bravo C. (2018). Editorial to the special issue on profit-driven analytics. Big Data.
Stripling E., Baesens B., Chizi B., Vanden Broucke S. (2018). Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers' compensation fraud. Decision Support Systems.
Lismont J., Van Calster T., Oskarsdottir M., Vanden Broucke S., Baesens B., Lemahieu W., Vanthienen J. (2018). Closing the gap between experts and novices using analytics-as-a-service: an experimental study. Business & Information Systems Engineering.
Verbeke W., Martens D., Baesens B. (2017). RULEM: rule learning with monotonicity constraints for ordinal classification. Applied Soft Computing.
Stripling E., Vanden Broucke S., Antonio K., Baesens B., Snoeck M. (2017). Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation.
Baesens B., Vanden Broucke S., Dejaeger K., Eerola T., Goedhuys L., Riis M., Wehkamp R. (2013). Cloudcomputing in analytics: de hype ontraadseld. Informatie (professional oriented).
Setiono R., Baesens B., Martens D., (2011). Rule extraction from neural networks and support vector machines for credit scoring.
Books
Baesens B., Backiel A., Vanden Broucke S. (2018). Java для начинающих. Объектно-ориентированный подход. Ozon.Ru.
Vanden Broucke S., Baesens B. (2017). Web scraping for data science with Python. CreateSpace Amazon. ISBN: 978-1979343787.
Scheule H., Roesch D., Baesens B. (2017). Credit risk analytics: the R companion. CreateSpace Amazon. ISBN: 1977760864.
Verbeke W., Bravo C., Baesens B. (2017). Profit driven business analytics - a practitioner’s guide to transforming big data into added value. Wiley. ISBN: 978-1119286554.
Baesens B., Roesch D., Scheule H. (2016). Credit Risk Analytics – Measurement Techniques, Applications and Examples in SAS. Wiley. ISBN: 978-1-119-14398-7.
Baesens B., Backiel A., Vanden Broucke S. (2015). Beginning Java programming: the object-oriented approach. Wrox. ISBN: 978-1118739495.
Baesens B., Van Vlasselaer V., Verbeke W. (2015). Fraud Analytics using Descriptive, Predictive & Social Network Techniques. Wiley. ISBN: 978-1-119-13312-4.
Baesens B. (2014). Analytics in a Big Data World. Wiley. ISBN: 978-1-118-89270-1.
Van Gestel T., Baesens B. (2009). Credit Risk Management: basic concepts: financial risk components, rating analysis, models, economic and regulatory capital. Oxford University Press. ISBN: 978-0-19-954511-7.
Accepted Books
Lemahieu W., Vanden Broucke S., Baesens B. (2018). Principles of database management – the practical guide to storing, managing and analyzing small and big data. Cambridge University Press.
Baesens B. (2016). 大数据分析-数据科学应用场景与实践精髓. Wiley.
Book Chapters
Vanthienen J., Baesens B., Chen G., Wei Q. (2015). Preface to the second international workshop on decision mining and modeling for business processes (DeMiMoP 2014). (xlvii-xlviii). (202). ISBN: 9783319158945.
Martens D., Baesens B. (2010). Building acceptable classification models. (53-74). (8). Springer.
Martens D., Huysmans J., Setiono R., Vanthienen J., Baesens B. (2008). Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. In: Diederich J. (Eds.), RULE EXTRACTION FROM SUPPORT VECTOR MACHINES(33-63). (Studies in Computational Intelligence, 80). SPRINGER-VERLAG BERLIN.
Martens D., Huysmans J., Setiono R., Vanthienen J., Baesens B. (2008). An overview of issues and application in credit scoring. In: Rule extraction from support vector machines, studies in computational intelligence(33-63). Springer. ISBN: 978-3-540-75389-6.
Baesens B., Mues C., Setiono R., De Backer M., Vanthienen J. (2003). Building intelligent credit scoring systems using decision tables. In: Camp O., Filipe J., Hammoudi S., Piattini M. (Eds.), Enterprise information systems V(131-137). Kluwer.
Baesens B., Setiono R., Mues C., Viaene S., Vanthienen J. (2001). Building intelligent credit-risk evaluation expert systems using neural network rule extraction and decision tables. In: Vandenbulcke J., Snoeck M. (Eds.), New directions in software engineering(121-133). Leuven (Belgium): Leuven University Press. ISBN: 90-5867-185-2.
Accepted Book Chapters
Verbraken T., Van Vlasselaer V., Verbeke W., Martens D., Baesens B. (2013). Advanced rule base learning: active learning, rule extraction, and incorporating domain knowledge. In: Coussement K., De Bock K., Neslin S. (Eds.), Advanced database marketing: innovative methodologies & applications for managing customer relationshipsLondon (UK): Gower Publishing. ISBN: 1409444619.
Conference Proceedings
De Winter S., Decuypere T., Mitrovic S., Baesens B., De Weerdt J. (2018). Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction. In: Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (1234-1241). ISBN: 9781538660515. doi: 10.1109/ASONAM.2018.8508272.
Haegemans T., Reusens M., Baesens B., Lemahieu W., Snoeck M. (2017). Towards a visual approach to aggregate data quality measurements. In: Proceedings of the International Conference on Information Quality Presented at the International Conference on Information Quality (ICIQ) 2017, Little Rock (US), 06 Oct 2017-07 Oct 2017.
De Koninck P., Nelissen K., Baesens B., Vanden Broucke S., Snoeck M., De Weerdt J. (2017). An approach for incorporating expert knowledge in trace clustering. In: Dubois E., Pohl K. (Eds.), Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings (Eds.) (561-576). Presented at the International Conference on Advanced Information Systems Engineering (CAiSE 2017), Essen (Germany), 12 Jun 2017-16 Jun 2017. ISBN: 978-3-319-59536-8.
Oskarsdottir M., Bravo C., Verbeke W., Sarraute C., Baesens B., Vanthienen J. (2016). A comparative study of social network classifiers for predicting churn in the telecommunication industry. In: Advances in Social Networks Analysis and Mining (ASONAM) (1151-1158). Presented at the The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, San Francisco (US), 18 Aug 2016-21 Aug 2016. ISBN: 978-1-5090-2846-7.
Vanthienen J., Baesens B., Chen G., Wei Q. (2016). Preface to the Third International Workshop on Decision Mining and Modeling for Business Processes (DeMiMoP'15). In: Reichert M., Reijers HA. (Eds.), BUSINESS PROCESS MANAGEMENT WORKSHOPS, (BPM 2015) (Eds.): vol. 256 (402-403). Presented at the 13th International Conference on Business Process Management Workshops (BPM), Univ Innsbruck, Innsbruck, AUSTRIA, 31 Aug 2015-03 Sep 2015. ISBN: 978-3-319-42886-4.
Stripling E., Vanden Broucke S., Antonio K., Baesens B., Snoeck M. (2015). Profit maximizing logistic regression modeling for customer churn prediction. In: Data Science and Advanced Analytics (DSAA) (1-10). Presented at the IEEE International Conference on Data Science and Advanced Analytics (DSAA' 2015), Paris (France), 19 Oct 2015-21 Oct 2015. Paris, France. doi: 10.1109/DSAA.2015.7344874.
Backiel A., Verbinnen Y., Baesens B., Claeskens G. (2015). Combining Local and Social Network Classifiers to Improve Churn Prediction. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (651-659). Presented at the International Conference on Advances in Social Networks Analysis and Mining, Paris (France), 25 Aug 2015-28 Aug 2015. New York (USA). ISBN: 978-1-4503-3854-7.
Pinheiro C., Van Vlasselaer V., Baesens B., Evsukoff AG., Silva MA H B., Ebecken NF F. (2015). A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data. In: Silhavy R., Senkerik R., Kominkova Oplatkova Z., Prokopova Z., Silhavy P. (Eds.)(35-44). Switzerland: Springer International Publishing. ISBN: 978-3-319-18472-2.
Van Vlasselaer V., Eliassi-Rad T., Akoglu L., Snoeck M., Baesens B. (2015). AFRAID: Fraud Detection via Active Inference in Time-Evolving Social Networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Network Analysis (659-666). Presented at the ASONAM, Paris (France), 25 Aug 2015-28 Aug 2015. ISBN: 978-1-4503-3854-7.
Van Vlasselaer V., Akoglu L., Eliassi-Rad T., Snoeck M., Baesens B. (2015). Guilt-by-constellation: fraud detection by suspicious clique memberships. In: Proceedings of 48 Annual Hawaii International Conference on System Sciences (918-927). Presented at the HICSS-48, Kauai (Hawaii), 05 Jan 2015-08 Jan 2015. doi: 10.1109/HICSS.2015.114.
Dirick L., Bellotti T., Claeskens G., Baesens B. (2015). The prediction of time to default for personal loans using mixture cure models: including macro-economic factors. In: Crook J. (Eds.), Proceedings of the Credit Scoring and Credit Control XIV conference (Eds.) (Paper No. 30) (0-0). Presented at the Credit Scoring and Credit Control XIII conference, Edinburgh (UK), 26 Aug 2015-28 Aug 2015.
Backiel A., Baesens B., Claeskens G. (2014). Mining telecommunication networks to enhance customer lifetime predictions. In: FEB Research Report KBI_1403 Leuven (Belgium).
Vanden Broucke S., Vanthienen J., Baesens B. (2014). Declarative process discovery with evolutionary computing. In: 2014 IEEE Congress on Evolutionary Computation Proceedings (2412-2419). Presented at the 2014 IEEE, Beijing (China), 06 Jul 2014-11 Jul 2014. ISBN: 978-1-4799-1488-3.
Vanden Broucke S., Munoz-Gama J., Carmona J., Baesens B., Vanthienen J. (2014). Event-based real-time decomposed conformance analysis. In: On the Move Federated Conferences & Workshops: vol. 8841 (345-363). Presented at the International Conference on Cooperative Information Systems (CoopIS 2014), Amantea, Calabria (Italy), 27 Oct 2014-31 Oct 2014. ISBN: 978-3-662-45550-0.
Zhu X., Zhu G., Vanden Broucke S., Vanthienen J., Baesens B. (2014). Towards location-aware process modeling and execution. In: Business Process Management Workshops (186-197). Presented at the Workshop on Data- & Artifact-Centric BPM (DAB’14), Haifa (Israel), 07 Sep 2014-11 Sep 2014.
Van Dongen BF., Weber B., Ferreira DR., De Weerdt J. (2014). Report: Business process intelligence challenge 2013. In: Lecture Notes in Business Information Processing: vol. 171 171 LNBIP (79-87). ISBN: 9783319062563. doi: 10.1007/978-3-319-06257-0.
Backiel A., Baesens B., Claeskens G. (2014). Mining telecommunication networks to enhance customer lifetime predictions. In: Lecture Notes in Artificial Intelligence: vol. 8468 (15-26). Presented at the Artificial Intelligence and Soft Computing – International Conference (ICAISC 2014), Zakopane (Poland), 01 Jun 2014-05 Jun 2014. ISBN: 978-3-319-07175-6.
Louis P., Baesens B. (2013). Do for-profit micro-finance institutions achieve better financial efficiency and social impact? A generalized estimating equations panel data approach. In: Financial Globalisation and Sustainable Finance: Implications for Policy and Practice conference (0-0). Presented at the Financial Globalisation and Sustainable Finance: Implications for Policy and Practice conference, Cape Town (South Africa), 29 May 2013-31 May 2013. Cape Town, South Africa.
De Weerdt J., Caron F., Vanthienen J., Baesens B. (2013). Getting a grasp on clinical pathway data: An approach based on process mining. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): vol. 7769 LNAI (22-35). ISBN: 9783642367779. doi: 10.1007/978-3-642-36778-6_3.
Van Vlasselaer V., Meskens J., Van Dromme D., Baesens B. (2013). Using social network knowledge for detecting spider constructions in social security fraud. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (819-826). Presented at the ASONAM, Niagara Falls (Canada), 25 Aug 2013-28 Aug 2013. 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331, USA. ISBN: 978-1-4503-2240-9.
Dirick L., Claeskens G., Baesens B. (2013). A new approach for variable selection in mixture cure models for prediction time of default. In: Proceedings of the Credit Scoring and Credit Control XIII conference (Paper No. 9) (0-0). Presented at the Credit Scoring and Credit Control XIII conference, Edinburgh, UK, 28 Aug 2013-30 Aug 2013.
Caron F., Vanthienen J., Baesens B. (2013). Healthcare analytics: examining the diagnosis-treatment cycle. In: CENTERIS 2013 - Conference on ENTERprise Information Systems / HCIST 2013 - International Conference on Health and Social Care Information Systems and Technologies: vol. 9 (996-1004). Presented at the HCIST 2013 - International Conference on Health and Social Care Information Systems and Technologies, Lisbon (Portugal), 23 Oct 2013-25 Oct 2013. doi: 10.1016/j.protcy.2013.12.111.
Li L., Goethals F., Baesens B. (2013). Predicting e-commerce adoption using data about product search and supplier search behavior. In: Crossing the Chasm of E-Business (111-120). Presented at the International Conference on Electronic Business (ICEB2013), Nanyang university, Singapore, 01 Dec 2013-04 Dec 2013.
Li L., Goethals F., Baesens B., Giangreco A. (2013). Using social network data to predict technology acceptance. Presented at the 2013 International Conference on Information Systems, Milan (Italy), 15 Dec 2013-18 Dec 2013.
Van Vlasselaer V., Meskens J., Van Dromme D., Baesens B. (2013). Using social network knowledge for detecting spider constructions in social security fraud. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (813-820). ISBN: 9781450322409. doi: 10.1145/2492517.2500292.
Vanden Broucke S., De Weerdt J., Vanthienen J., Baesens B. (2013). A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, part of the IEEE Symposium Series on Computational Intelligence 2013, SSCI 2013 (254-261). Presented at the IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2013), Singapore, 16 Apr 2013-19 Apr 2013. New York, USA. ISBN: 978-1-4673-5895-8.
Vanden Broucke SK L M., Vanthienen J., Baesens B. (2013). Third international business process intelligence challenge (BPIC'13): Volvo IT Belgium VINST. In: CEUR Workshop Proceedings: vol. 1052.
Caron F., Vanthienen J., Baesens B. (2013). Business rule patterns and their application to process analytics. In: Proceedings of the 8th International Workshop on Vocabularies, Ontologies and Rules for the Enterprise and Beyond (VORTE 2013) (13-20). Presented at the International Workshop on Vocabularies, Ontologies and Rules for the Enterprise and Beyond (VORTE 2013), Vancouver (US), 09 Sep 2013-13 Sep 2013. ISBN: 978-1-4799-3048-7.
Vanden Broucke S., Vanthienen J., Baesens B. (2013). Volvo IT Belgium VINST. In: Proceedings of the 3rd Business Process Intelligence Challenge co-located with 9th International Business Process Intelligence workshop (BPI 2013): vol. 1052 (Paper No. 3) (0-0). Presented at the Business Process Intelligence Challenge 2013 (BPIC 2013), Beijing (China), 26 Aug 2013-26 Aug 2013. Aachen (Germany).
Vanden Broucke S., Delvaux C., Freitas J., Rogova T., Vanthienen J., Baesens B. (2013). Uncovering the relationship between event log characteristics and process discovery techniques. In: Business Process Management Workshops (41-53). Presented at the Workshop on Business Process Intelligence (BPI2013), Beijing (China), 26 Aug 2013-30 Aug 2013. ISBN: 978-3-319-06256-3.
Vanden Broucke S., Caron F., Vanthienen J., Baesens B. (2013). Validating and enhancing declarative business process models based on allowed and non-occurring past behavior. In: Business Process Management Workshops (212-223). Presented at the Workshop on Decision Mining & Modeling for Business Processes (DeMiMoP’13), Beijing (China), 26 Aug 2013-30 Aug 2013.
De Weerdt J., Caron F., Vanthienen J., Baesens B. (2013). Getting a grasp on clinical pathway data: an approach based on process mining. In: Emerging Trends in Knowledge Discovery and Data Mining (LNAI 7769) (22-35). Presented at the PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust and DSDM, Kuala Lumpur (Malaysia), 29 May 2012-01 Jun 2012. Berlin Heidelberg. doi: 10.1007/978-3-642-36778-6.
Seret A., Vanden Broucke S., Baesens B., Vanthienen J. (2013). An exploratory approach for understanding customer behavior processes bases on clustering and sequence mining. In: Business Process Management Workshops (237-248). Presented at the Workshop on Decision Mining & Modeling for Business Processes (DeMiMoP’13), Beijing (China), 26 Aug 2013-30 Aug 2013. ISBN: 978-3-319-06257-0.
Caron F., Vanden Broucke S., Vanthienen J., Baesens B. (2012). On the distinction between truthful, invisible, false and unobserved events: An event existence classification framework and the impact on business process analytics related research areas. In: 18th Americas Conference on Information Systems 2012, AMCIS 2012: vol. 1 (50-60). ISBN: 9781622768271.
Verbraken T., Verbeke W., Weber R., Bravo C., Baesens B. (2012). A profit based performance measure for consumer credit scoring models. In: Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012 (225-227). ISBN: 9789728939694.
De Weerdt J., Vanden Broucke S., Vanthienen J., Baesens B. (2012). Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. In: FEB Research Report KBI_1215 Leuven (Belgium).
Caron F., Vanthienen J., Baesens B. (2012). Rule-Based business process mining applications for management. In: Casillas J., Martínez-Lopéz FJ., Corchado JM. (Eds.), Proceedings of the International Symposium on Management Intelligent Systems (Eds.): vol. 171 (273-282). Presented at the International Symposium on Management Intelligent Systems, Salamanca (Spain), 11 Jul 2012-13 Jul 2012. Heidelberg. ISBN: 978-3-642-30863-5.
Caron F., Vanthienen J., De Weerdt J., Baesens B. (2012). Advanced care-flow mining and analysis. In: Daniel F., Barkaoui K., Dustdar S. (Eds.), Lecture Notes in Business Information Processing (Eds.): vol. 99 (167-168). Presented at the Business Process Management Workshops (BPM 2011), Clermont-Ferrand (France), 28 Aug 2011-02 Sep 2011. Heidelberg. ISBN: 978-3-642-28107-5.
Verbraken T., Goethals F., Verbeke W., Baesens B. (2012). Using social network classifiers for predicting e-commerce adoption. In: E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life: vol. 108 (9-21). Presented at the The Tenth Workshop on E-Business (WEB2011), Shanghai (China), 04 Dec 2011-04 Dec 2011. ISBN: 978-3-642-29872-1.
Vanden Broucke S., De Weerdt J., Baesens B., Vanthienen J. (2012). An improved artificial negative event generator to enhance process event logs. In: Ralyt J., Franch X., Brinkkemper S., Wrycza S. (Eds.), Lecture Notes in Computer Science (Eds.) (254-269). Presented at the International Conference on Advanced Information Systems Engineering (CAiSE'12), Gdansk (Poland), 25 Jun 2012-29 Jun 2012. ISBN: 978-3-642-31094-2.
Moges HT., Lemahieu W., Baesens B. (2012). The use of data quality information (DQI) for decision-making: an exploratory study. In: Nag A. (Eds.), Proceedings of the International Conference on Business Management and Information Systems (ICBMIS 2012) (Eds.) (386-394). Presented at the International conference on business management and information systems (ICBMIS 2012), Singapore, 22 Nov 2012-24 Nov 2012. New Delhi 110 070.
Caron F., Vanden Broucke S., Vanthienen J., Baesens B. (2012). On the distinction between truthful, invisible, false and unobserved events. In: Proceedings of the 18th Americas Conference on Information Systems (Paper No. 24). Presented at the Americas Conference on Information Systems, Seattle, Washington (US), 09 Aug 2012-12 Aug 2012. ISBN: 978-0-615-66346-3.
De Weerdt J., Vanden Broucke S., Vanthienen J., Baesens B. (2012). Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. In: Evolutionary Computation (CEC), 2012 IEEE Congress on (1-8). Presented at the Congress on Evolutionary Computation (CEC), 2012 IEEE, Brisbane (Australia), 10 Jun 2012-15 Jun 2012. ISBN: 978-1-4673-1510-4.
Louis P., Van Laere E., Baesens B. (2011). Motivating and predicting bank rating transitions using optimal survival analysis models. In: Proceedings of the 24th Australasian Finance & Banking Conference Presented at the Australasian Finance & Banking Conference, Sydney (Australia), 14 Dec 2011-16 Dec 2011. Australia. ISBN: 0-9873127-3-1.
Louis P., Van Laere E., Baesens B. (2011). Predicting bank rating transitions using optimal competing risks survival analysis models. In: Proceedings of the credit scoring and credit control XII conference Presented at the Credit Scoring and Credit Control conference, Edinburgh (UK), 24 Aug 2011-26 Aug 2011. Edinburgh, UK.
De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2011). A robust F-measure for evaluating discovered process models. In: CIDM (148-155). Presented at the IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology (CompSens)/IEEE Symposium Series on Computational Intelligence (SSCI), Paris (France), 11 Apr 2011-15 Apr 2011. ISBN: 978-1-4244-9925-0.
Baojun M., Dejaeger K., Vanthienen J., Baesens B. (2011). Software defect prediction based on association rule classification. In: FBE Research Report KBI_1105 Leuven (Belgium).
Dejaeger K., Hamers B., Poelmans J., Baesens B. (2010). A novel approach to the evaluation and improvement of data quality in the financial sector. In: Proceedings of 15th International Conference on Information Quality (ICIQ 2010) Presented at the International Conference on Information Quality (ICIQ 2010), Little Rock, Arkansas (US), 12 Nov 2010-14 Nov 2010.
Castermans G., Martens D., Van Gestel T., Hamers B., Baesens B. (2010). An overview and framework for PD backtesting and benchmarking. In: Journal of the Operational Research Society: vol. 61 (3) (359-373).
Vanhoutte C., Martens D., De Winne S., Sels L., Baesens B. (2010). The initial resource-performance relationship in new ventures: Towards a configurational approach. In: Proceedings of the 7th International AGSE Entrepreneurship Research Exchange (CD-rom) (147-161). Presented at the AGSE International Entrepreneurship Research Exchange, Queensland (Australia), 02 Feb 2010-05 Feb 2010. Queensland (Australia). ISBN: 978-0-9803328-6-5.
Setiono R., Dejaeger K., Verbeke W., Martens D., Baesens B. (2010). Software effort prediction using regression rule extraction from neural networks. In: Proceedings 22nd International Conference on Tools with Artificial Intelligence ICTAI 2010: vol. 2 (45-52). Presented at the International Conference on Tools with Artificial Intelligence (IEEE-ICTAI), Arras (France), 27 Oct 2010-29 Oct 2010. ISBN: 978-0-7695-4263-8.
De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2010). A critical evaluation study of model-log metrics in process discovery. In: Zur Muehlen M., Su J. (Eds.), Business Process Management Workshops (Eds.): vol. 66 (158-169). Presented at the Workshop on Business Process Intelligence (BPI2010), New Jersey (US), 14 Sep 2010-16 Sep 2010. ISBN: 978-3-642-20510-1.
Verbeke W., Baesens B., Martens D., De Backer M., Haesen R. (2009). Including domain knowledge in customer churn prediction using AntMiner+. In: Perner P. (Eds.), Advances in data mining in marketing (Eds.) (10-21). Presented at the Industrial Conference on Data Mining, Leipzig (Germany), 20 Jul 2009-22 Jul 2009. ISBN: 978-3-940501-07-3.
Wessa P., Baesens B. (2009). Fraud detection in statistics education based on the compendium and reproducible computing. In: Burgin M., Chowdhury MH., Ham CH., Ludwig S., Su W., Yenduri S. (Eds.), IEEE Proceedings of the world congress on computer science and information engineering (Eds.) (50-54). Presented at the World Congress on Computer Science and Information Engineering, Los Angeles, California, USA, 31 Mar 2009-02 Apr 2009. Washington. ISBN: 978-0-7695-3507-4.
Wessa P., Baesens B. (2009). Fraud detection in statistics education based on the compendium platform and reproducible computing. In: IEEE proceedings of the world congress on computer science and information engineering Presented at the World congress on computer science and information engineering, Los Angeles/Anaheim (USA), 01 Jan 2009-01 Jan 2009.
Baesens B., Mues C., Martens D., Vanthienen J. (2008). 50 years of data mining and OR: Upcoming trends and challenges. In: 50th Annual Conference of the Operational Research Society 2008, OR50 (67-79).
Goedertier S., Martens D., Baesens B., Haesen R., Vanthienen J. (2008). Process Mining as First-Order Classification Learning on Logs with Negative Events. In: Lecture Notes in Computer Science: vol. 4928 (42-53). Presented at the Workshop on Business Process Intelligence (BPI 07) at BPM 2007, Brisbane, Australia, 24 Sep 2007-24 Sep 2007.
Vanthienen J., Martens D., Goedertier S., Baesens B. (2008). Placing process intelligence within the business intelligence framework. (Paper No. 8). Presented at the EIS 2008, Atlanta (US), 14 Apr 2008-18 Apr 2008.
Baesens B., Setiono R., Mues C. (2007). Neural network rule extraction and decision tables for software fault prediction. In: Lecture Notes in Computer Science Presented at the Fourteenth International Conference on Neural Information Processing (ICONIP 2007), Special session on 'Innovation in Machine Learning and Data Mining', Kitakyushu, Japan.
Setiono R., Baesens B., Mues C. (2006). Risk management and regulatory compliance: a data mining framework based on neural network rule extraction. In: Proceedings of the International Conference on Information Systems (ICIS 2006) (71-85). Presented at the the International Conference on Information Systems (ICIS 2006), Milwaukee, Wisconsin (US), 10 Dec 2006-13 Dec 2006.
Huysmans J., Baesens B., Vanthienen J. (2006). ITER: An algorithm for predictive regression rule extraction. In: Lecture Notes in Computer Science: vol. 4081 (270-279). Presented at the International Conference on Data Warehousing and Knowledge Discovery (DAWAK), Krakow (Poland).
Mues C., Baesens B., Huysmans J., Vanthienen J. (2006). Comprehensible Credit-Scoring Knowledge Visualization using Decision Tables and Diagrams. In: Secura I., Cordeiro J., Hammoudi S., Filipe J. (Eds.), Enterprise Information Systems (Eds.): vol. 6 (109-115). Presented at the 6th International Conference on Enterprise Information Systems (ICEIS 2004), Oporto, 14 Apr 2004-17 Apr 2004.
Huysmans J., Martens D., Baesens B., Vanthienen J., Van Gestel T. (2006). Country corruption analysis with self organizing maps and support vector machines. In: Lecture Notes in Computer Science: vol. 3917 (103-114). Presented at the 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.
Martens D., De Backer M., Haesen R., Baesens B., Mues C., Vanthienen J. (2006). Ant-based approach to the knowledge fusion problem. In: Lecture Notes in Computer Science: vol. 4150 (84-95). Presented at the International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 2006), Brussels, Belgium.
Egmont-Petersen M., Feelders A., Baesens B. (2005). Confidence intervals for probabilistic network classifiers. In: Computational statistics & data analysis: vol. 49 (4) (998-1019).
Van Gestel T., Suykens J., Pelckmans K., Baesens B. (2005). Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines. In: Workshop on Machine Learning in Finance, NIPS 2005 Conference.
De Backer M., Haesen R., Martens D., Baesens B. (2005). A stigmergy based approach to data mining. In: Lecture Notes in Computer Science: vol. 3809 (975-978). Presented at the Australian Joint Conference on Artificial Intelligence (AI 2005), Sydney, Australia.
Mues C., Baesens B., Setiono R., Vanthienen J. (2005). From knowledge discovery to implementation: A business intelligence approach using neural network rule extraction and decision tables. In: Lecture Notes in Computer Science, Professional Knowledge Management: Third Biennial Conference, WM 2005, Kaiserslautern, Germany, April 10-13, 2005, Revised Selected Papers: vol. 3782 (483-495).
Meeus N., Huysmans J., Baesens B., Vanthienen J., Vandebroek M. (2005). The use of knowledge discovery techniques for behavioural scoring. Presented at the International Conference on Data Mining, Text Mining and their Business Applications, Skiathos (Greece), 25 May 2005-27 May 2005.
Huysmans J., Baesens B., Vanthienen J. (2005). A comprehensible SOM-based scoring system. In: Perner P., Imiya A. (Eds.), Lecture Notes in Artificial Intelligence (Eds.): vol. 3587 (80-89). Presented at the International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2005), Leipzig (Germany).
Huysmans J., Baesens B., Vanthienen J. (2004). The influence of caching on web usage mining. In: Data Miniing V ; Data mining, text mining and their business applications (77-86). Presented at the International conference on data mining DATA MINING V, Malaga (Spain), Sep. 15-17, Southampton, Boston. ISBN: 978-1-85312-722-9.
Mues C., Baesens B., Files C., Vanthienen J. (2004). Decision diagrams in machine learning: an empirical study on real-life credit-risk data. In: Lecture Notes in Computer Science: vol. 2980 (395-397). Presented at the International Conference, Diagrams 2004, Cambridge, United Kingdom.
Mues C., Baesens B., Huysmans J., Vanthienen J. (2004). Comprehensible credit-scoring knowledge visualization using decision tables and diagrams. In: Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS2004) (226-232). Presented at the International Conference on, Porto (Portugal), 13 Apr 2004-14 Apr 2004.
Huysmans J., Baesens B., Mues C., Vanthienen J. (2004). Web usage mining with time constrained association rules. In: Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS2004) (343-348). Presented at the International Conference on Enterprise Information Systems, Porto (Portugal), 13 Apr 2004-14 Apr 2004.
Van Gestel T., Baesens B., Suykens J., Baestaens D., Vanthienen J., De Moor B. (2003). Bankruptcy prediction with least squares support vector machine classifiers. In: Proc. of the International Conference on Computational Intelligence for Financial Engineering (CIFER'03) (1-8). Presented at the International Conference on Computational Intelligence for Financial Engineering (CIFER'03, Hong Kong, China, 01 Mar 2003-01 Mar 2003. Leuven. ISBN: 0-7803-7654-4.
Baesens B., Mues C., De Backer M., Vanthienen J., Setiono R. (2003). Building intelligent credit scoring systems using decision tables. In: ICEIS 2003 - Proceedings of the 5th International Conference on Enterprise Information Systems: vol. 2 (19-25). ISBN: 9729881618.
Baesens B., Mues C., Setiono R., De Backer M., Vanthienen J. (2003). Building intelligent credit scoring systems using decision tables- Best paper nomination. In: Proceedings of the Fifth International Conference on Enterprise Information Systems (ECEIS'2003) (19-25). Presented at the International Conference on Enterprise Information Systems (ECEIS'2003), Angers (France), 19 Apr 2003-25 Apr 2003. ISBN: 9729881618.
Viaene S., Derrig RA., Baesens B., Dedene G. (2002). A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. Presented at the Insurance Claim Fraud, Rhode Island (USA), 01 Nov 2002-01 Nov 2002.
Buckinx W., Baesens B., Van den Poel D., Van Kenhove P., Vanthienen J. (2002). Using machine learning techniques to preduct defection of top clients. In: Data mining III (509-517). Presented at the International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields, Bolgona (Italy), 23 Sep 2002-25 Sep 2002. Ashurst, Southampton. ISBN: 1853129259.
Viaene S., Baesens B., Dedene G., Vanthienen J., Van Den Poel D. (2002). Proof running two state-of-the-art pattern recognition techniques in the field of direct marketing. In: ICEIS 2002 - Proceedings of the 4th International Conference on Enterprise Information Systems: vol. 1 (446-454). ISBN: 9729805067.
Baesens B., Egmont-Petersen M., Castelo R., Vanthienen J. (2002). Learning Bayesian network classifiers for credit scoring using Markov chain Monte Carlo search. In: Proceedings of the Sixth International Conference on Pattern Recognition (ICPR'2002) Presented at the International Conference on Pattern Recognition (ICPR'2002), Québéc (Canada), 11 Aug 2002-15 Aug 2002. Washington. ISBN: 978-3-540-74195-4.
Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J. (2002). Proof running two state-of-the-art pattern recognition techniques in the field of direct marketing. In: Piattini M., Filipe J., Braz J. (Eds.)(125-133). Deventer: Kluwer Eds.. ISBN: 978-1-4020-1086-6.
Verstraeten G., Baesens B., Van den Poel D., Egmont-Petersen M., Van Kenhove P., Vanthienen J. (2002). Targeting long-life customers: towards a segmented CRM approach. In: Proceedings of the Thirty-First European Marketing Academy Conference (EMAC'2002) on 'Marketing in a changing world: scope, opportunities and challenges' (2321-2324). Presented at the European Marketing Academy Conference (EMAC'2002) on 'Marketing in a changing world: scope, opportunities and challenges', Braga (Portugal), 28 May 2002-31 May 2002.
Viaene S., Baesens B., Van den Poel D., Vanthienen J., Dedene G. (2001). The Bayesian evidence framework for database marketing modeling using both RFM and non-RFM predictors. In: World Multiconference on Systemics, Cybernetics and Informatics Presented at the World Multi-Conference on Systemics, Cybernetics and Informatics (SCI), Orlando, Florida (USA), 22 Jul 2001-25 Jul 2001. New York, USA. ISBN: 980-07-7541-2.
Baesens B., Setiono R., Mues C., Viaene S., Vanthienen J. (2001). Building credit-risk evaluation expert systems using neural network rule extraction and decision tables. In: Proceedings of the Twenty Second International Conference on Information Systems (ICIS) (159-168). Presented at the International Conference on Information Systems (ICIS), New Orleans, LA (USA), 18 Jun 2001-19 Jun 2001.
Baesens B., Viaene S., Vanthienen J. (2000). Post-processing of association rules. Presented at the Conference on Knowledge Discovery in Databases, Turawa (Poland), 01 May 2000-01 May 2000.
Viaene S., Baesens B., Van Gestel T., Suykens J., Dedene D., De Moor B., Vanthienen J. (2000). Least squares support vector machine classifiers : An empirical evaluation. In: Proceedings of the 12th Belgian-Dutch Artificial Intelligence Conference (BNAIC) Presented at the 12th Belgian-Dutch Artificial Intelligence Conference (BNAIC), Kaatsheuvel (The Netherlands), 01 Nov 2000-01 Nov 2000.
Baesens B., Viaene S., Vanthienen J., Dedene G. (2000). Wrapped feature selection by means of guided neural network optimization. Presented at the 15th International Conference on Pattern Recognition (ICPR), Barcelona (Spain), 03 Sep 2000-08 Sep 2000.
Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J. (2000). Wrapped feature selection for binary classification Bayesian regularisation neural networks: a database marketing application. Presented at the International Conference on Data Mining, Cambridge (UK), 05 Jul 2000-07 Jul 2000.
Baesens B., Viaene S., Van Gestel T., Suykens J., Dedene G., De Moor B., Vanthienen J. (2000). An Empirical assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers. In: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES) Presented at the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES), Brighton, UK, 30 Aug 2000-01 Sep 2000.
Viaene S., Baesens B., Dedene G., Vanthienen J., Vandenbulcke J. (2000). Sensitivity based pruning of input variables by means of weight cascaded retraining. Presented at the International Conference and Exhibition on the Practical Application of Knowledge Discovery and Data Mining (PADD), Manchester (UK), 10 Apr 2000-14 Apr 2000.
Baesens B., Viaene S., Vanthienen J. (2000). Post-processing of association rules. In: The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), special workshop on post-processing in machine learning and data mining: interpretation, visualization, integration, and related topics Presented at the The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Boston, Ma (USA), 20 Aug 2000-23 Aug 2000.
Accepted Conference Proceedings
Höppner S., Stripling E., Baesens B., Vanden Broucke S., Verdonck T. (2018). Profit Driven Decision Trees for Churn Prediction. In: Proceedings of the conference on Data Science, Statistics and Visualisation (DSSV 2018) Presented at the Conference on Data Science, Statistics and Visualisation (DSSV 2018), Vienna, Austria.
Stripling E., Baesens B., Vanden Broucke S. (2018). Regularized Empirical EMP Maximization Framework for Profit-Driven Model Building. In: not known yet Presented at the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, United Kingdom.
Devos A., Dhondt J., Stripling E., Baesens B., Vanden Broucke S., Sukhatme G. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. In: Proceedings of the IEEE Data Science Workshop (DSW) Presented at the IEEE Data Science Workshop (DSW), Lausanne, Switzerland.
Mitrovic S., Singh G., Baesens B., Lemahieu W., De Weerdt J. (2017). Scalable rfm-enriched representation learning for churn prediction. In: Proceedings of the fourth IEEE International Conference on Data Science and Advanced Analytics (DSAA2017) Presented at the IEEE International Conference on Data Science and Advanced Analytics (DSAA2017), Tokyo (Japan), 19 Oct 2017-21 Oct 2017.
Bing Z., Vanden Broucke S., Baesens B., Maldonado S. (2017). Improving resampling-based ensemble in Churn prediction. In: Proceedings of the first international workshop on Learning With Imbalanced Domains: Theory and Applications (LIDTA 2017) Presented at the International workshop on Learning With Imbalanced Domains: Theory and Applications (LIDTA 2017), Skopje (Macedonia), 22 Sep 2017-22 Sep 2017.
Mitrovic S., Baesens B., Lemahieu W., De Weerdt J. (2017). Churn prediction using dynamic rfm-augmented node2vec. In: Proceedings of the Third international workshop on Dynamics in and of Networks, ECML-PKDD 2017 Presented at the International workshop on Dynamics in and of Networks, ECML-PKDD 2017, Skopje (Macedonia), 18 Sep 2017-22 Sep 2017.
Van Calster T., Lismont J., Oskarsdottir M., Vanden Broucke S., Vanthienen J., Lemahieu W., Baesens B. (2016). Automated analytics: the organizational impact of analytics-as-a-service. In: Proceedings of the EI-KDD’16 workshop Presented at the Workshop on Enterprise Intelligence in conjunction with 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco (US), 14 Aug 2016-14 Aug 2016.
Moges HT., Dejaeger K., Lemahieu W., Baesens B. (2011). Data quality for credit risk management: new insights and challenges. Presented at the International Conference on Information Quality (ICIQ) 2011, University of South Australia, Adelaide (Australia), 18 Nov 2011-20 Nov 2011.
Abstracts/Presentations/Posters
Reynkens T., Antonio K., Devriendt S., Baesens B., Vanden Broucke S. (2018). Network-based fraud detection in insurance using GOTCHA!. Presented at the 4th European Actuarial Journal Conference, Leuven.
Reusens M., Baesens B., Lemahieu W., Sels L. (2018). Identifying successful search patterns for improved job recommendation. Presented at the Conference on Business Analytics in Finance and Industry (BAFI), Santiago (Chile), 17 Jan 2018-19 Jan 2018.
Lismont J., Ram S., Baesens B., Lemahieu W., Vanthienen J. (2018). The (pseudo-)social behavior of products in offline retail stores: predicting increase in product interpurchase time. Presented at the Conference on Business Analytics in Finance and Industry (BAFI), Santiago (Chile), 17 Jan 2018-19 Jan 2018.
Van Calster T., Reusens M., Baesens B., Lemahieu W. (2018). Forecasting blood donations with neural networks. Presented at the Business analytics in finance and industry, Santiago (Chili), 17 Jan 2018-19 Jan 2018.
Oskarsdottir M., Bravo C., Verbeke W., Sarraute C., Baesens B., Vanthienen J. (2017). Churn prediction in the telecommunication industry using social network analytics. (162-164). Presented at the NetMob 2017, Milan (Italy), 05 Apr 2017-07 Apr 2017.
Oskarsdottir M., Van Calster T., Lemahieu W., Baesens B., Vanthienen J. (2017). Clustering time series from call networks to predict churn. Presented at the ENBIS-17, Naples (Italy), 09 Sep 2017-14 Sep 2017.
Van Calster T., Reusens M., Oskarsdottir M., Mitrovic S., Lismont J., De Weerdt J., Lemahieu W., Baesens B., Vanthienen J. (2017). What's in the network? A stepwise overview of working with networked data in R. Presented at the useR!, Brussels (Belgium), 05 Jul 2017-07 Jul 2017.
Mitrovic S., Baesens B., Lemahieu W., De Weerdt J. (2017). On added value of feature engineering for churn prediction. Presented at the NetMob 2017, Milan (Italy), 05 Apr 2017-07 Apr 2017.
Haupt J., Stripling E., Baesens B., Vanden Broucke S., Lessmann S. (2017). Profit-maximizing scorecard development. Presented at the Credit Scoring and Credit Control XV Conference, Edinburgh (Scotland), 30 Aug 2017-01 Sep 2017.
Van Calster T., Lemahieu W., Baesens B. (2016). Data-driven algorithm for bottom-up hierarchical forecasting: a global sales forecast for the Coca-Cola Company. Presented at the Business Analytics in Finance and Industry, Santiago (Chile), 14 Dec 2015-16 Dec 2015.
Reusens M., Seret A., Baesens B., Lemahieu W., Sels L. (2015). Application of a personalized collaborative filtering job recommender system for the Flemish employment service. Presented at the European Conference on Operational Research, Glasgow (United Kingdom), 12 Jul 2015-15 Jul 2015.
Van Calster T., Lemahieu W., Baesens B. (2015). Hierarchical sales forecasting for The Coca-Cola Company - A time series benchmark and initial tool optimization. Presented at the European Conference on Operational Research, Glasgow (United Kingdom), 12 Jul 2015-15 Jul 2015.
Lismont J., Vanthienen J., Baesens B., Lemahieu W. (2015). The role of the data scientist in the modern organization. Presented at the European Conference on Operational Research, Glasgow (United Kingdom), 12 Jul 2015-15 Jul 2015.
Oskarsdottir M., Vanthienen J., Baesens B., Van Vlasselaer V., Backiel A. (2015). Effects of community-based churn detection in the telecom sector. Presented at the European Conference on Operational Research, Glasgow (UK), 12 Jul 2015-15 Jul 2015.
Dirick L., Claeskens G., Baesens B. (2015). Credit risk modeling using mixture cure models: variable selection and time-dependent covariates. Presented at the One-day workshop on multivariate survival data, Hasselt (Belgium), 25 Jun 2015-25 Jun 2015.
Dirick L., Claeskens G., Vasnev A., Baesens B. (2014). Using mixture cure models with unobserved heterogeneity for the analysis of credit loan data. Presented at the International Conference on Computational Statistics (COMPSTAT 2014), Geneva (Switzerland), 19 Aug 2014-22 Aug 2014.
Dirick L., Claeskens G., Vasnev A., Baesens B. (2014). Modeling unobserved heterogeneity in mixture cure models. Presented at the Meeting of the Belgian Statistical Society (BSS), Louvain-la-Neuve (Belgium), 05 Nov 2014-07 Nov 2014.
Caron F., Vanthienen J., Baesens B. (2014). Modeling business decisions and processes – which comes first?. Presented at the Conference of the International Federation of Operational Research Societies, Barcelona (Spain), 13 Jul 2014-18 Jul 2014.
Van Vlasselaer V., Akoglu L., Eliassi-Rad T., Snoeck M., Baesens B. (2014). Finding cliques in large fraudulent networks: theory and insights. Presented at the Conference of the International Federation of Operational Research Societies (IFORS 2014), Barcelona (Spain), 13 Jul 2014-18 Jul 2014.
Van Vlasselaer V., Akoglu L., Eliassi-Rad T., Snoeck M., Baesens B. (2014). Gotch’all! Advanced network analysis for detecting groups of fraud. Presented at the PAW (Predictive Analytics World), London (UK), 29 Oct 2014-30 Oct 2014. (professional oriented).
Dirick L., Claeskens G., Baesens B. (2013). The analysis of credit risk data: an information criterion for multiple event mixture cure models. Presented at the LStat, Leuven (Belgium), 13 Dec 2013-14 Dec 2013.
Dirick L., Claeskens G., Baesens B. (2013). Performing model selection in mixture cure models for the analysis of credit risk data. Presented at the European Meeting of Statisticians, Budapest (Hungary), 20 Jul 2013-25 Jul 2013.
Dirick L., Claeskens G., Baesens B. (2013). The analysis of credit risk data: variable selection for a mixture cure model. Presented at the Workshop on Model Selection, Nonparametrics and Dependence Modeling, Rennes (France), 08 Jul 2013-09 Jul 2013.
Seret A., Maldonado S., Weber R., Baesens B. (2013). Knowledge based feature selection for unsupervised learning: theory and application. Presented at the European Conference on Operational Research, Rome (Italy), 01 Jul 2013-04 Jul 2013.
Van Vlasselaer V., Baesens B. (2013). Improving fraud detection techniques using social network analytics for the Belgian government. Presented at the PAW (Predictive Analytics World), London (UK), 23 Oct 2013-24 Oct 2013. (professional oriented).
Baesens B., Van Vlasselaer V. (2013). Social network analytics for fraud detection: insights and challenges. Presented at the SAS Analytics, Orlando, Florida (US), 21 Oct 2013-22 Oct 2013. (professional oriented).
Dejaeger K., Verbraken T., Baesens B. (2012). Assessing Bayesian network classifiers for software defect prediction. Presented at the EURO 2012 conference, Vilnius (Latvia), 09 Jul 2012-11 Jul 2012.
Verbraken T., Lessmann S., Baesens B. (2012). Toward profit-driven churn modeling with predictive marketing analytics. Presented at the Workshop on E-Business (WEB2012), Orlando (US), 15 Dec 2012-15 Dec 2012.
Caron F., Vanden Broucke S., Vanthienen J., Baesens B. (2012). On the distinction between truthful, invisible, false and unobserved events. In: Sprouts: Working Papers on Information Systems: vol. 12 (16). Presented at the 11th JAIS Theory Development Workshop at ICIS 2012, Orlando, Florida, 16 Dec 2012-16 Dec 2012.
Van Laere E., Baesens B. (2011). Analyzing bank ratings: key determinants and procyclicality. Presented at the Annual Australasian Finance and Banking Conference, Sydney (Australia), 14 Dec 2011-16 Dec 2011.
Verbeke W., Verbraken T., Martens D., Baesens B. (2011). Relational learning for customer churn prediction: the complementarity of networked and non-networked classifiers. Presented at the Conference on the Analysis of Mobile Phone Datasets and Networks, Cambridge (US), 10 Oct 2011-11 Oct 2011.
Verbeke W., Dejaeger K., Verbraken T., Martens D., Baesens B. (2011). Mining social networks for customer churn prediction. Presented at the Interdisciplinary Workshop on Information and Decision in Social Networks, Cambridge (US), 31 May 2011-01 Jun 2011.
Baojun M., Dejaeger K., Vanthienen J., Baesens B. (2010). Software defect prediction based on association rule classification. Presented at the International Conference on Electronic-Business Intelligence (ICEBI2010), Kunming (China), 19 Dec 2010-21 Dec 2010.
Verbeke W., Dejaeger K., Baesens B. (2010). Comparing classification techniques to forecast customer churn. Presented at the OR52 Annual Conference, London (UK), 07 Sep 2010-09 Sep 2010.
Setiono R., Dejaeger K., Verbeke W., Martens D., Baesens B. (2010). Software effort prediction using regression rule extraction from neural networks. Presented at the OR52 Annual Conference, London (UK), 07 Sep 2010-09 Sep 2010.
Verbeke W., Dejaeger K., Martens D., Baesens B. (2010). Customer churn prediction: does technique matter?. Presented at the Joint Statistical Meeting, Vancouver (Canada), 31 Jul 2010-05 Aug 2010.
Verbeke W., Berteloot K., Castermans G., Martens D., Van Gestel T., Baesens B. (2010). Modeling credit rating migrations dependent on the business cycle. Presented at the EURO 2010, Lisbon (Portugal), 11 Jul 2010-14 Jul 2010.
Van Laere E., Baesens B. (2010). The development of a simple and intuitive rating system under solvency II. Presented at the Midwest Finance Association 2010 Conference, Las Vegas (US), 24 Feb 2010-27 Feb 2010.
Dejaeger K., Verbeke W., Huysmans J., Mues C., Vanthienen J., Baesens B. (2010). Rule based predictive models, decision table and tree: an empirical evaluation on comprehensibility. Presented at the EURO 2010, Lisbon (Portugal), 11 Jul 2010-14 Jul 2010.
Verbeke W., Martens D., Baesens B. (2009). Building comprehensible customer churn prediction models with advanced rule induction techniques. Presented at the Dag van het Vlaams Wetenschappelijk Economisch Onderzoek, Hasselt (Belgium), 30 Oct 2009-30 Oct 2009.
Van Gool J., Baesens B., Sercu P., Verbeke W. (2009). An analysis of the applicability of credit scoring for microfinance. Presented at the Academic and Business Research Institute Conference, Orlando (US), 24 Sep 2009-26 Sep 2009.
Verbeke W., Baesens B., Martens D., De Backer M., Haesen R. (2009). Building accurate, comprehensible, and justifiable customer churn prediction models using AntMiner+. Presented at the Joint Statistical Meeting, Washington D.C. (US), 01 Aug 2009-06 Aug 2009.
Martens D., Van Gestel T., Vanden Branden K., Jacobs J., Baesens B. (2009). A practical framework for credit risk stress testing. (1-10). Presented at the Conference on Credit Scoring and Credit Control, Edinburgh (UK), 26 Aug 2009-28 Aug 2009.
Loterman G., Brown I., Martens D., Mues C., Baesens B. (2009). Benchmarking state-of-the-art regression algorithms for loss given default modelling. (1-10). Presented at the Conference on Credit Scoring and Credit Control, Edinburgh (UK), 26 Aug 2009-28 Aug 2009.
Van Laere E., Baesens B. (2009). Regulatory and economic capital: theory and practice, evidence from the field. (1-10). Presented at the International Risk Management Conference 2009, Financial instability. A new world framework?, Venice (Italy), 22 Jun 2009-24 Jun 2009.
Wessa P., Baesens B. (2009). Explorative data mining of constructivist learning experiences and activities with multiple dimensions. (413-418). Presented at the ICCIT 2009, Dubai, United Arab Emirates, 28 Jan 2009-30 Jan 2009.
Vuylsteke A., Wen Z., Baesens B., Poelmans J. (2009). Consumers online information search: a cross-cultural study between China and Western Europe. (1-14). Presented at the Academic And Business Research Institute Conference 2009, Orlando (US), 24 Sep 2009-26 Sep 2009.
Wessa P., Baesens B., Poelmans S., Van Stee E. (2009). Reproducible computing workshop. Presented at the Applied Statistics, Ribno (Bled), Slovenia, 20 Sep 2009-23 Sep 2009.
Vanhoutte C., Martens D., Sels L., Maes J., Baesens B. (2008). Resource configurations for top performing start-ups: An exploratory study with classification trees. Presented at the Babson College Entrepreneurship Research Conference, Chapel Hill (USA), 05 Jun 2008-07 Jun 2008.
Van Laere E., Baesens B. (2008). The development of a simple and intuitive rating system under Solvency II. (1-10). Presented at the International Risk Management Conference (IRMC 2008), Credit and Financial Risk Management: 40 years after the Altman Z-score model, Florence (Italy), 12 Jun 2008-14 Jun 2008.
Glady N., Baesens B., Croux C. (2007). A modified pareto/NBD approach for predicting customer lifetime value. (1-10). Presented at the Statistics for Data Mining, Learning and Knowledge Extraction (IASC 07) Conference, Aveiro (Portugal), 30 Aug 2007-01 Sep 2007.
Castermans G., Martens D., Van Gestel T., Hamers B., Baesens B. (2007). Quantitative Validation: An Overview and Framework for PD Backtesting and Benchmarking. Presented at the Credit Scoring and Credit Control X, Edinburgh, Scotland (U.K.).
Martens D., Vanthienen J., Goedertier S., Baesens B. (2007). Placing process intelligence within the business intelligence framework. Presented at the International Conference, Lhasa, Tibet (China), 01 Jul 2007-06 Jul 2007.
Martens D., Vanthienen J., Baesens B., Mues C. (2006). Measuring the consistency with prior knowledge of classification models. Presented at the Operational Research Conference, Bath (UK), 11 Sep 2006-11 Sep 2006.
Martens D., Baesens B., Mues C., Hsin-Vonn S., Shahi R., Vanthienen J. (2006). Building acceptable classifiers with ants. Presented at the European Conference on Operational Research, Reykjavik (Iceland), 04 Jul 2006-04 Jul 2006.
Martens D., De Backer M., Haesen R., Baesens B. (2005). On the use of ant systems for data mining. Chester, U.K..
Huysmans J., Baesens B., Vanthienen J. (2005). Country corruption analysis with SOMs and SVMs. Chester (U.K.), Sep. 13-15.
Martens D., Baesens B., Van Gestel T., Vanthienen J. (2005). Adding comprehensibility to support vector machine models using rule extraction techniques (poster). Edinburgh (U.K.), Sept. 7-9.
Martens D., Baesens B., Van Gestel T., Vanthienen J. (2005). Benchmarking state-of-the-art classification techniques for credit scoring. Brest (France), May 20.
Baesens B., Van De Walle P., Callewaert B., Huenaerts C., Molenaers G., Meeusen C., Nijs J., Desloovere K. (2004). A study on maturation of oxygen rate and cost during walking and the influence of net non-dimensional normalization using sitting and standing data. Presented at the Annual meeting of European Society for Movement Analysis of Adulst an Children, Warshau, Poland, 23 Sep 2004-25 Sep 2004.
Huysmans J., Baesens B., Vanthienen J. (2004). Web usage mining: a practical study. (86-99). Presented at the Twelfth Conference on Knowledge Acquisition and Management, Kule (Poland), 13 May 2004-15 May 2004.
Viaene S., Derrig R., Baesens B., Dedene G. (2002). New developments in insurance fraud detection modeling: a comparison of state- of- the- art classification techniques for expert automobile insurance fraud detection. Presented at the Rosen - Huebner - McCahan Seminar Series, Penn State University Park, Pennsylvania (USA), 01 Mar 2002-01 Mar 2002.
Viaene S., Derrig R., Baesens B., Dedene G. (2001). A comparison of state-of-the-art classification techniques for expert automobile insurance fraud detection. Presented at the International Congress on Insurance: Mathematics and Economics, Pennsylvania (USA), 23 Jul 2001-25 Jul 2001.
Baesens B., Viaene S., Vanthienen J. (2001). A comparative study of state of the art classification algorithms for credit scoring. Presented at the Conference on Credit Scoring and Credit Control (CSCCVII), Edinburgh (Scotland), 01 Sep 2001-01 Sep 2001.
Baesens B., Setiono R., De Lille V., Viaene S., Vanthienen J. (2001). Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables. (Abstract No. 20). Presented at the Pacific Asian Conference on Intelligent Systems (PACIS), Seoul (Korea), 20 Jun 2001-22 Jun 2001.
Accepted Abstracts/Presentations/Posters
Oskarsdottir M., Bravo C., Baesens B., Vanthienen J. (2018). Social network analytics in micro-lending. Presented at the European Conference on Operational Research.
Oskarsdottir M., Van Calster T., Baesens B., Lemahieu W., Vanthienen J. (2018). A representation of dynamic networks for early churn detection in telco. Presented at the NetSci 2018, Paris (France), 11 Jun 2018-15 Jun 2018.
Oskarsdottir M., Bravo C., Vanthienen J., Baesens B. (2018). Behavior based time-to-default predictions. Presented at the Conference on Business Analytics in Finance and Industry (BAFI), Santiago (Chile), 17 Jan 2018-19 Jan 2018.
Stripling E., Baesens B., Vanden Broucke S. (2018). Building profit-sensitive classifiers for maximum profit. Presented at the European Conference on Operational Research (EURO 2018), Valencia (Spain), 08 Jul 2018-11 Jul 2018.
Oskarsdottir M., Bravo C., Verbeke W., Baesens B., Vanthienen J. (2018). Effects of network architecture on model performance when predicting churn in telco. Presented at the NetMob 2017, Milan (Italy), 05 Apr 2017-07 Apr 2017.
Oskarsdottir M., Bravo C., Verbeke W., Sarraute C., Baesens B., Vanthienen J. (2017). Profit driven comparison of social network analytics methods for predicting customer churn in telco. Presented at the 2017 INFORMS Annual Meeting, Houston, Texas (US), 22 Oct 2017-25 Oct 2017.
Lismont J., Baesens B., Lemahieu W., Vanthienen J. (2017). Discovering communities in customer purchase behavior by means of social network analytics. Presented at the Annual Conference of the European Network for Business and Industrial Statistics (ENBIS), Naples (Italy), 09 Sep 2017-14 Sep 2017.
Van Calster T., Reusens M., Baesens B., Lemahieu W. (2016). Forecasting blood donations with Google Trends. Presented at the International Symposium on Forecasting, Santander (Spain), 19 Jun 2016-22 Jun 2016.
Dirick L., Claeskens G., Baesens B. (2015). Advances on the use of mixture cure models in the credit risk context. Presented at the CFE-ERCIM International Conference on Computational and Financial Econometrics (CFE 2015), and on Computational and Methodological Statistics (CMStatistics 2015), London (United Kingdom), 12 Dec 2015-14 Dec 2015.
Van Vlasselaer V., Van Dromme D., Baesens B. (2013). Social network analysis for detecting spider constructions in social security fraud: new insights and challenges. Presented at the European Conference on Operational Research, Rome (Italy), 01 Jul 2013-04 Jul 2013.
Vantieghem J., Van Laere E., Baesens B. (2013). The difference between Moody's and S&P bank ratings: is discretion in the rating process causing a split?. Presented at the International IFABS conference, Nottingham (UK), 26 Jun 2013-28 Jun 2013.
Louis P., Baesens B. (2013). Do for-profit micro-finance institutions achieve better financial efficiency and social impact?. Presented at the European Research Conference on Microfinance, Kristiansand (Norway), 10 Jun 2013-12 Jun 2013.
Science Outreach
Lismont J., Van Calster T., Oskarsdottir M., Vanthienen J., Baesens B., Lemahieu W. (2015). API for prediction and machine learning: poll results and analysis. KDnuggets News, Art.No. 29.
Baesens B., Backiel A., Vanden Broucke S. (2015). The state of database access in Java: Passchendaele revisited. Cutter IT Email Advisor (CIT Advisor).
Vanden Broucke S., Baesens B., Vanthienen J. (2013). Closing the loop: state of the art in business process analytics. Data Insight & Social BI: Executive Update.
Dejaeger K., Vanden Broucke S., Eerola T., Wehkamp R., Goedhuys L., Riis M., Baesens B. (2012). Beyond the hype: cloud computing in analytics.
Dejaeger K., Vanden Broucke S., Eerola T., Wehkamp R., Goedhuys L., Riis M., Baesens B. (2012). Beyond the hype: cloud computing in analytics. Data Insight & Social BI: Executive Update.
PhD Theses
Stripling E., Snoeck M. (sup.), Vanden Broucke S. (cosup.), Baesens B. (cosup.) (2018). Business-Oriented Data Analytics: Advances in Profit-Driven Model Building and Fraud Detection.
Oskarsdottir M., Vanthienen J. (sup.), Baesens B. (cosup.) (2018). Leveraging Mobile Phone Data and Social Network Analytics for Profit Driven Modeling..
Van Calster T., Lemahieu W. (sup.), Baesens B. (cosup.) (2018). A matter of time - leveraging time series data for business applications..
Reusens M., Lemahieu W. (sup.), Baesens B. (cosup.) (2018). Towards a better understanding of recommender system in the labor market..
Lismont J., Vanthienen J. (sup.), Lemahieu W. (cosup.), Baesens B. (cosup.) (2018). From bit to business: Addressing managerial and practical challenges of analytics adoption. 217.
Li L., Baesens B. (sup.), Snoeck M. (cosup.), Goethals F. (cosup.) (2017). Essays on relational and time to event analysis..
Dirick L., Claeskens G. (sup.), Baesens B. (cosup.) (2015). Contributions to the analysis of credit risk data using advanced survival analysis techniques..
Van Vlasselaer V., Baesens B. (sup.), Snoeck M. (cosup.) (2015). FAIR: Forecasting and network analytics for collection risk management..
Seret A., Baesens B. (sup.) (2015). Business-driven data mining: new algorithms and applications..
Moges HT., Lemahieu W. (sup.), Baesens B. (cosup.) (2014). A contextual data quality analysis for credit risk management in financial institutions..
Vanden Broucke S., Baesens B. (sup.), Vanthienen J. (cosup.) (2014). Advances in Process Mining: Artificial negative events and othertechniques..
Louis P., Baesens B. (sup.) (2013). Case Studies in Quantitative Financial Modeling..
Verbraken T., Baesens B. (sup.) (2013). Business oriented data analytics: theory and case studies. 213.
Caron F., Vanthienen J. (sup.), Baesens B. (cosup.) (2013). Business process analytics for enterprise risk management and auditing. 303.
Dejaeger K., Baesens B. (sup.), Snoeck M. (cosup.) (2012). Essays on empirical software engineering..
De Weerdt J., Baesens B. (sup.), Vanthienen J. (cosup.) (2012). Business process discovery: new techniques and applications..
Verbeke W., Baesens B. (sup.), Martens D. (cosup.) (2012). Profit driven data mining in massive customer networks: new insights and algorithms..
Van Laere E., Baesens B. (sup.) (2011). Capital regulation of financial institutions, the role of ratings and the tension field between regulation and economic reality..
Glady N., Croux C. (sup.), Baesens B. (cosup.) (2008). Customer profitability modeling..
Martens D., Baesens B. (sup.), Vanthienen J. (sup.) (2008). Building acceptable classification models for financial engineering applications.
Baesens B. (2003). Developing intelligent systems for credit scoring using machine learning techniques.
Reports
Reusens M., Haegemans T., Lemahieu W., Snoeck M., Baesens B., Sels L. (2017). Understanding recommendation quality using embeddings. FEB Research Report KBI_1712, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Dirick L., Bellotti T., Claeskens G., Baesens B. (2016). Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models. KBI_1630, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Vanden Broucke S., Vanthienen J., Baesens B. (2014). Straightforward Petri net-based event log generation in ProM. FEB Research Report KBI_1417, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Vanden Broucke S., De Weerdt J., Vanthienen J., Baesens B. (2013). On replaying process execution traces containing positive and negative events. FEB Research Report KBI_1311, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Caron F., Vanthienen J., Baesens B. (2013). Advances in rule-based process mining: applications for enterprise risk management and auditing. FEB Research Report KBI_1305, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Vanden Broucke S., Muñoz-Gama J., Carmona J., Baesens B., Vanthienen J. (2013). Event-based real-time decomposed conformance analysis. Polytechnic University of Catalonia, Department of Information Languages and Systems.
Caron F., Vanthienen J., Baesens B. (2012). A comprehensive framework for the application of process mining in risk management and compliance checking. FEB Research Report KBI_1226, Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Vanden Broucke S., De Weerdt J., Vanthienen J., Baesens B. (2012). An improved process event log artificial negative event generator. FEB Research Report KBI_1216, 1-17. Leuven (Belgium): KU Leuven - Faculty of Economics and Business.
Magerman T., Van Looy B., Baesens B., Debackere K. (2011). Assessment of Latent Semantic Analysis (LSA) text mining algorithms for large scale mapping of patent and scientific publication documents. FBE Research Report MSI_1114, Leuven (Belgium): K.U.Leuven - Faculty of Business and Economics.
Glady N., Baesens B., Croux C. (2007). A modified Pareto/NBD approach for predicting customer lifetime value. DTEW - KBI_0726, 1-23. K.U.Leuven - Faculty of Economics and Applied Economics.
Goedertier S., Martens D., Baesens B., Haesen R., Vanthienen J. (2007). A new approach for discovering business process models from event logs. DTEW - KBI_0716, 1-20. K.U.Leuven - Faculty of Economics and Applied Economics.
Huysmans J., Setiono R., Baesens B., Vanthienen J. (2007). A new approach for the extraction of knowledge from opaque predictive models. DTEW - KBI_0711, 1-20. K.U.Leuven - Faculty of Economics and Applied Economics.
Cumps B., Martens D., De Backer M., Haesen R., Viaene S., Dedene G., Baesens B., Snoeck M. (2007). Predicting business/ICT alignment with AntMiner+. DTEW - KBI_0708, 1-29. K.U.Leuven - Faculty of Economics and Applied Economics.
Huysmans J., Baesens B., Vanthienen J. (2006). Using rule extraction to improve the comprehensibility of predictive models. DTEW - KBI_0612, 1-55. K.U.Leuven - Faculty of Economics and Applied Economics.
Glady N., Baesens B., Croux C. (2006). Modeling customer loyalty using customer lifetime value. DTEW - KBI_0618, 1-22. K.U.Leuven - Faculty of Economics and Applied Economics.
Martens D., Baesens B., Van Gestel T., Vanthienen J. (2005). Comprehensible credit scoring models using rule extraction from support vector machines. DTEW Research Report 0581, 1-21. K.U.Leuven - Departement toegepaste economische wetenschappen.
Van Gestel T., Baesens B., Suykens J., Van den Poel D., Baestaens D., Willekens M. (2004). Bayesian Kernel-based classification for financial distress detection. Ghent University, Department of Marketing Working Paper 04/247, Gent: Department of Marketing, Ghent University.
Van Gestel T., Baesens B., Van Dijcke P., Garcia J., Suykens J., Vanthienen J. (2004). A process model to develop an internal rating system: sovereign credit ratings. Internal Report 04-130, Leuven (Belgium): ESAT-SISTA, K.U.Leuven.
Van Gestel T., Espinoza M., Baesens B., Suykens J., Brasseur C., De Moor B. (2004). A Bayesian nonlinear support vector machine error correction model. Internal Report 04-140, Leuven (Belgium): ESAT-SISTA, K.U.Leuven.
Mues C., Baesens B., Files CM., Vanthienen J. (2004). Decision diagrams in machine learning: an empirical study on real-life credit-risk data. DTEW Research Report 0405, 1-20. K.U.Leuven - Departement toegepaste economische wetenschappen.
Baesens B., Verstraeten G., Van den Poel D., Egmont-Petersen M., Van Kenhove P., Vanthienen J. (2002). Bayesian network classifiers for identifying the slope of customer-lifecycle of long-life customers. Ghent University, Department of Marketing Working Paper 02/154, Gent: Department of Marketing, Ghent University.
Viaene S., Baesens B., Van den Poel D., Vanthienen J., Dedene G. (2001). Bayesian neural network learning for repeat purchase modelling in direct marketing. DTEW Research Report 0114, 1-40. K.U.Leuven - Departement toegepaste economische wetenschappen.
Baesens B., Viaene S., Vanthienen J. (2000). Post-processing of association rules. DTEW Research Report 0020, 1-18. Leuven: K.U.Leuven.
Viaene S., Baesens B., Van den Poel D., Dedene G., Vanthienen J. (2000). Wrapped feature selection for neural networks in direct marketing. DTEW Research Report 0019, 1-14. Leuven: K.U.Leuven.
Baesens B., Viaene S., Van Gestel T., Suykens J., Dedene G., De Moor B., Vanthienen J. (2000). Least squares support vector machine classifiers: an empirical evaluation. DTEW Research Report 0003, 1-16. K.U.Leuven - Departement toegepaste economische wetenschappen.
Viaene S., Baesens B., Dedene G., Vanthienen J., Vandenbulcke J. (1999). Sensivity based pruning of input variables by means of weight cascaded retraining. DTEW Research Report 9954, 1-21. K.U.Leuven - Departement toegepaste economische wetenschappen.
Other
Dejaeger K., Verbeke W., Martens D., Baesens B. (2010). De kosten van software-ontwikkeling voorspellen. Informatie, 52 (9), 8-13.
Dejaeger K., Ruelens J., Van Gestel T., Jacobs J., Baesens B., Poelmans J., Hamers B. (2009). Evaluatie en verbetering van de datakwaliteit. Informatie, 51 (9), 8-15.
De Backer M., Baesens B. (2009). BPMN 2.0: meer dan een naamsverandering ?. Informatie, 37-43.
Verbeke W., Baesens B. (2009). Van credit crunch naar ICT crash, of niet ?. Data News, Art.No. 1.
Vuylsteke A., Poelmans J., Baesens B. (2009). Online zoekgedrag van consumenten: China vs West-Europa. Business In-Zicht, 2009, 1-2.
Baesens B. (2007). It’s the data, you stupid!. Data News, 25.
Haesen R., Martens D., De Backer M., Baesens B. (2005). AntMiner+, een systeem van kennis-ontginnende mieren. Business Inzicht (20), 1-4.
Van Gestel T., Baesens B., Vanthienen J. (2004). De impact van Bazel II op IT. Informatie: Maandblad voor de informatievoorziening, 46 (10), 50-55.
Baesens B., Mues C., Vanthienen J. (2003). Knowledge discovery in data: naar performante én begrijpelijke modellen van bedrijfsintelligentie. Business Inzicht (12).
Baesens B., Mues C., Vanthienen J. (2003). Knowledge discovery in data: van academische denkoefening naar bedrijfsrelevante praktijk. Informatie: Maandblad voor de informatievoorziening, 45 (1), 30-35.
Accepted Other
Dejaeger K., Verbeke W., Martens D., Baesens B. (2010). Het voorspellen van software-ontwikkelkosten. Informatie.
Baesens B., De Backer M., Martens D. (2009). Business intelligence + process management = business process intelligence. Informatie.