Publications

Journal Articles

• Zhu Z., Wang X., Baesens B. (2019). On the Optimal Marketing Aggressiveness Level of C2C Sellers in Social Media: Evidence from China. The International Journal of Management Science, 85, 83-93. 
• Dirick L., Bellotti T., Claeskens G., Baesens B. (2019). Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models. Journal of Business and Economic Statistics, 37 (1), 40-53. doi: 10.1080/07350015.2016.1260471. 
• Baesens B., Vanden Broucke S., Höppner S., Verdonck T., Stripling E. (2018). Profit Driven Decision Trees for Churn Prediction. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, doi: 10.1016/j.ejor.2018.11.072. 
• 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.
• Baesens, Bart, Verbeke, W, & Bravo, C. (2018). Editorial to the special issue on profit-driven analytics. Big Data, Big Data; 2018.
• Baesens, Bart, Verbeke, Wouter, & Bravo, Cristian. (2018). Special Issue on Profit-Driven Analytics. Big Data, 6(1), 1-2.
• 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, 108, 13-24.
• Nelissen, Klaas, Snoeck, Monique, Vanden Broucke, Seppe, & Baesens, Bart. (2018). Swipe and tell: Using implicit feedback to predict user engagement on tablets. ACM Transactions on Information Systems, ACM Transactions on Information Systems; 2018.
• Reusens, M, Lemahieu, W, Baesens, B, & Sels, L. (2018). Evaluating recommendation and search in the labor market. Knowledge-Based Systems, 152, 62-69.
• 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, 104, 22-32.
• Lismont, Jasmien, Van Calster, Tine, Oskarsdottir, María, Vanden Broucke, Seppe, Baesens, Bart, Lemahieu, Wilfried, & Vanthienen, Jan. (2018). Closing the gap between experts and novices using analytics-as-a-service: An experimental study. Business Information Systems Engineering, Business Information Systems Engineering; 2018.
• 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, 111, 13-26.
• Óskarsdóttir, 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.
• Zhu, Z, Wang, X, & Baesens, Bart. (2018). On the Optimal Marketing Aggressiveness Level of C2C Sellers in Social Media: Evidence from China. The International Journal of Management Science, Accepted, The International Journal of Management Science; 2018; Vol. accepted.
• Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2018). On the operational efficiency of different feature types for telco Churn prediction. European Journal of Operational Research, 267(3), 1141-1155.
• Oskarsdottir, Maria, Baesens, Bart, & Vanthienen, Jan. (2018). Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values. Big Data,6(1), 53-65.
• Stripling, Eugen, Vanden Broucke, Seppe, Antonio, Katrien, Baesens, Bart, & Snoeck, Monique. (2018). Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation, 40, 116-130.
• Zhu, Bing, Baesens, Bart, Backiel, Aimee, & Vanden Broucke, Seppe KLM. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 69(1), 49-65.
• Verbeke, Wouter, Martens, David, & Baesens, Bart. (2017). RULEM: Rule learning with monotonicity constraints for ordinal classification. Applied Soft Computing, Applied Soft Computing; 2017.
• Zhu, Bing, Niu, Yongge, Xiao, Jin, & Baesens, Bart. (2017). A new transferred feature selection algorithm for customer identification. Neural Computing and Applications,28(9), 2593-2603.
• De Winne, Sophie, Baesens, Bart, & Sels, Luc. (2017). Evidence based HRM en HR analytics: Een stand van zaken. Over.werk. Tijdschrift Van Het Steunpunt WSE, 02, 51-55.
• Zhu, Bing, Baesens, Bart, & Vanden Broucke, Seppe KLM. (2017). An empirical comparison of techniques for the class imbalance problem in churn prediction. Information Sciences, Applications, 408, 84-99.
• Verbeke, Wouter, Martens, David, & Baesens, Bart. (2017). RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Applied Soft Computing, 60, 858-873.
• Reusens, Michael, Lemahieu, Wilfried, Baesens, Bart, & Sels, Luc. (2017). A note on explicit versus implicit information for job recommendation. Decision Support Systems,98, 26-35.
• Van Calster, Tine, Baesens, Bart, & Lemahieu, Wilfried. (2017). ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing, 60, 775-785.
• Oskarsdottir, Maria, Bravo, Cristian, Verbeke, Wouter, Sarraute, Carlos, Baesens, Bart, & Vanthienen, Jan. (2017). Social network analytics for churn prediction in telco: Model building, evaluation and network architecture. Expert Systems with Applications, 85, 204-220.
• Mendling, Jan, Baesens, Bart, Bernstein, Abraham, & Fellmann, Michael. (2017). Challenges of smart business process management: An introduction to the special issue. Decision Support Systems, 100, 1-5.
• Baesens, Bart, De Winne, Sophie, & Sels, Luc. (2017). Is Your Company Ready for HR Analytics? Sloan Management Review, 58(2), 20-21.
• Van Vlasselaer, Veronique, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique, & Baesens, Bart. (2017). GOTCHA! Network-Based Fraud Detection for Social Security Fraud. Management Science, 63(9), 3090-3110.
• 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, 1-14.
• Zhu, B, Baesens, Bart, Backiel, Aimée, & Vanden Broucke, Seppe. (2017). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 1-17.
• Li, Libo, Goethals, Frank, Baesens, Bart, & Snoeck, Monique. (2017). Predicting software revision outcomes on GitHub using structural holes theory. Computer Networks,114, 114-124.
• Lismont, Jasmien, Vanthienen, Jan, Baesens, Bart, & Lemahieu, Wilfried. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114-124.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2017). Time to default in credit scoring using survival analysis: A benchmark study. Journal of the Operational Research Society, 68(6), 652-665.
• Baesens, Bart, Bapna, R, Marsden, J, Vanthienen, Jan, & Zhao, J. (2016). Transformational issues of big data and analytics in networked business. MIS Quarterly,40(4), 1-12.
• Lismont, Jasmien, Vanthienen, Jan, Baesens, Bart, & Lemahieu, Wilfried. (2016). De uitdaging die Big Data heet: Pijnpunten en richtlijnen bij integratie van Big Data-analytics. AG Connect, 4, 72-75.
• Moges, Helen-Tadesse, Van Vlasselaer, Veronique, Lemahieu, Wilfried, & Baesens, Bart. (2016). Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes - An exploratory study. Decision Support Systems, 83, 32-46.
• Zhu, Xinwei, Vanden Broucke, Seppe, Zhu, Guobin, Vanthienen, Jan, & Baesens, Bart. (2016). Enabling flexible location-aware business process modeling and execution. Decision Support Systems, 83, 1-9.
• Baesens, Bart, De Winne, Sophie, & Sels, Luc. (2016). What to do before you fire a pivotal employee. Harvard Business Review, Harvard Business Review; 2016.
• Baesens, Bart, Bapna, Ravi, Marsden, James R, Vanthienen, Jan, & Zhao, J Leon. (2016). TRANSFORMATIONAL ISSUES OF BIG DATA AND ANALYTICS IN NETWORKED BUSINESS. MIS Quarterly, 40(4), 807-818.
• Vanden Broucke, Seppe KLM, Caron, Filip, Lismont, Jasmien, Vanthienen, Jan, & Baesens, Bart. (2016). On the gap between reality and registration: A business event analysis classification framework. Information Technology & Management, 17(4), 393-410.
• Backiel, Aimee, Baesens, Bart, & Claeskens, Gerda. (2016). Predicting time-to-chum of prepaid mobile telephone customers using social network analysis. Journal of the Operational Research Society, 67(9), 1135-1145.
• Maldonado, Sebastián, Flores, Álvaro, Verbraken, Thomas, Weber, Richard, & Baesens, Bart. (2015). Profit-based feature selection using support vector machines - general framework and an application for customer retention. Applied Soft Computing, 35, 740-748.
• Seret, Alex, Bejinaru, Andreea, & Baesens, Bart. (2015). Domain knowledge based segmentation of online banking customers. Intelligent Data Analysis, 19(S1), S163-S184.
• Van Vlasselaer, Veronique, Bravo, Cristian, Caelen, Olivier, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique, & Baesens, Bart. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2015). An Akaike information criterion for multiple event mixture cure models. European Journal of Operational Research, 24, 449-457.
• Lessmann, Stefan, Baesens, Bart, Seow, Hsin-Vonn, & Thomas, Lyn C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.
• Maldonado, Sebastian, Flores, Alvaro, Verbraken, Thomas, Baesens, Bart, & Weber, Richard. (2015). Profit-based feature selection using support vector machines - General framework and an application for customer retention. Applied Soft Computing, 35, 740-748.
• Caron, Filip, Vanthienen, Jan, Vanhaecht, Kris, Van Limbergen, Erik, De Weerdt, Jochen, & Baesens, Bart. (2015). A process mining based investigation of adverse events in care processes. Health Information Management Journal, 43(1), 16-25.
• Seret, Alex, Maldonado, Sebastian, & Baesens, Bart. (2015). Identifying next relevant variables for segmentation by using feature selection approaches. Expert Systems with Applications, 42(15-16), 6255-6266.
• Moeyersoms, Julie, De Fortuny, Enric Junque, Dejaeger, Karel, Baesens, Bart, & Martens, David. (2015). Comprehensible software fault and effort prediction: A data mining approach. Journal of Systems and Software, 100, 80-90.
• Minnaert, Bart, Martens, David, De Backer, Manu, & Baesens, Bart. (2015). To tune or not to tune: Rule evaluation for metaheuristic-based sequential covering algorithms. Data Mining and Knowledge Discovery, 29(1), 237-272.
• Vanden Broucke, Seppe, Baesens, Bart, Lismont, Jasmien, & Vanthienen, Jan. (2014). Sluit de lus: Moderne technieken in Business Process Analytics. Informatie, 56(1), 36-45.
• Van Molle, Ellen, Vanderloock, An, De Weerdt, Jochen, Lemahieu, Wilfried, Sels, Luc, Baesens, Bart, . . . Bouckaert, Dominiek. (2014). Efficiëntere loopbaanbegeleiding. Informatie, 49-53.
• Verbraken, Thomas, Goethals, Frank, Verbeke, Wouter, & Baesens, Bart. (2014). Predicting online channel acceptance with social network data. Decision Support Systems, 63, 104-114.
• Verbraken, Thomas, Verbeke, Wouter, & Baesens, Bart. (2014). Profit optimizing customer churn prediction with Bayesian network classifiers. Intelligent Data Analysis,18(1), 3-24.
• Seret, Alex, Verbraken, Thomas, & Baesens, Bart. (2014). A new knowledge-based constrained clustering approach: Theory and application in direct marketing. Applied Soft Computing, 24, 316-327.
• 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.
• Verbraken, Thomas, Bravo, Cristian, Weber, Richard, & Baesens, Bart. (2014). Development and application of consumer credit scoring models using profit-based V classification measures. European Journal of Operational Research, 238(2), 505-513.
• Verbeke, Wouter, Martens, David, & Baesens, Bart. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14(PART C), 431-446.
• Seret, Alex, Vanden Broucke, Seppe KLM, Baesens, Bart, & Vanthienen, Jan. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41(10), 4648-4657.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2014). Clinical Pathway Analytics. Journal of Information Technology Research, 7(1), 12-26.
• Caron, Filip, Vanthienen, Jan, Vanhaecht, Kris, Van Limbergen, Erik, De Weerdt, Jochen, & Baesens, Bart. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44(1), 88-96.
• Tobback, Ellen, Martens, David, Van Gestel, Tony, & Baesens, Bart. (2014). Forecasting Loss Given Default models: Impact of account characteristics and the macroeconomic state. Journal of the Operational Research Society, 65(3), 376-392.
• Ma, Baojun, Zhang, Huaping, Chen, Guoqing, Zhao, Yanping, & Baesens, Bart. (2014). Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective. International Journal of Software Engineering and Knowledge Engineering,24(1), 61-90.
• Caron, Filip, Vanthienen, Jan, Vanhaecht, Kris, Van Limbergen, Erik, Deweerdt, Jochen, & Baesens, Bart. (2014). A process mining-based investigation of adverse events in care processes. Health Information Management Journal, 43(1), 16-25.
• Vanden Broucke, Seppe KLM, De Weerdt, Jochen, Vanthienen, Jan, & Baesens, Bart. (2014). Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Transactions on Knowledge and Data Engineering,26(8), 1877-1889.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Comprehensive rule-based compliance checking and risk management with process mining decision support systems. Decision Support Systems, 54(3), 1357-1369.
• Baesens, Bart, Vanden Broucke, Seppe, Dejaeger, Karel, Eerola, T, Goedhuys, L, Riis, M, & Wehkamp, R. (2013). Cloudcomputing in analytics: De hype ontraadseld. Informatie, Informatie; 2013.
• Louis, Philippe, & Baesens, Bart. (2013). Do for-profit microfinance institutions achieve better financial efficiency and social impact? A generalised estimating equations panel data approach. Journal of Development Effectiveness, 5(3), 359-380.
• De Weerdt, Jochen, Schupp, Annelies, Vanderloock, An, & Baesens, Bart. (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.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Comprehensive rule-based compliance checking and risk management with process mining. Decision Support Systems, 54(3), 1357-1369.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Computers in Industry, 64(4), 464-475.
• Louis, Philippe, Seret, Alex, & Baesens, Bart. (2013). Financial efficiency and social impact of microfinance institutions using self-organizing maps. Newsletter of the Centre for Financial Services 0.
• Louis, Philippe, Van Laere, Elisabeth, & Baesens, Bart. (2013). Understanding and predicting bank rating transitions using optimal survival analysis models. Economics Letters, 119(3), 280-283.
• Berteloot, Koen, Verbeke, Wouter, Castermans, Gerd, Van Gestel, Tony, Martens, David, & Baesens, Bart. (2013). A Novel Credit Rating Migration Modeling Approach Using Macroeconomic Indicators. Journal of Forecasting, 32(7), 654-672.
• Moges, Helen-Tadesse, Dejaeger, Karel, Lemahieu, Wilfried, & Baesens, Bart. (2013). A multidimensional analysis of data quality for credit risk management: New insights and challenges. Information and Management, 50(1), 43-58.
• Verbraken, Thomas, Verbeke, Wouter, & Baesens, Bart. (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.
• De Weerdt, Jochen, Vanden Broucke, Seppe, Vanthienen, Jan, & Baesens, Bart. (2013). Active Trace Clustering for Improved Process Discovery. IEEE Transactions on Knowledge and Data Engineering, 25(12), 2708-2720.
• Dejaeger, Karel, Verbraken, Thomas, & Baesens, Bart. (2013). Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers. IEEE Transactions on Software Engineering, 39(2), 237-257.
• Tsujitani, M, & Baesens, Bart. (2012). Survival analysis for personal loan data using generalized additive models. Behaviormetrika, 39(1), 1-15.
• Seret, Alex, Verbraken, Thomas, Versailles, Sebastien, & Baesens, Bart. (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.
• De Weerdt, Jochen, De Backer, Manu, Vanthienen, Jan, & Baesens, Bart. (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.
• Loterman, Gert, Brown, Iain, Martens, David, Mues, Christophe, & Baesens, Bart. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28(1), 161-170.
• Baesens, B, Bouboulis, P, Cruces, S, Domeniconi, C, Ikeda, S, Li, X, . . . Liu, D. (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.
• Dejaeger, Karel, Goethals, Frank, Giangreco, Antonio, Mola, Lapo, & Baesens, Bart. (2012). Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 218(2), 548-562.
• Mehta, Vikas, Rycyna, Kevin, Baesens, Bart MM, Barkan, Gueliz A, Paner, Gladell P, Flanigan, Robert C, . . . Venkataraman, Girish. (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.
• Van Gool, Joris, Verbeke, Wouter, Sercu, Piet, & Baesens, Bart. (2012). Credit scoring for microfinance: Is it worth it? International Journal of Finance and Economics, 17(2), 103-123.
• Verbeke, Wouter, Dejaeger, Karel, Martens, David, Hur, Joon, & Baesens, Bart. (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, Karel, Verbeke, Wouter, Martens, David, & Baesens, Bart. (2012). Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Transactions on Software Engineering, 38(2), 375-397.
• De Weerdt, Jochen, Schupp, Annelies, Vanderloock, An, & Baesens, Bart. (2011). Datagedreven analyseren van bedrijfsprocessen op basis van process mining. Informatie,53(6), 34-39.
• Baesens, Bart, Martens, David, Setiono, R, & Zurada, J. (2011). White box nonlinear prediction models, editorial special issue. IEEE Transactions on Neural Networks,22(12), 2406-2408.
• Martens, David, Baesens, Bart, & Fawcett, Tom. (2011). Editorial survey: Swarm intelligence for data mining. Machine Learning, 82(1), 1-42.
• Martens, David, Vanthienen, Jan, Verbeke, Wouter, & Baesens, Bart. (2011). Performance of classification models from a user perspective. Decision Support Systems,51(4), 782-793.
• Huysmans, Johan, Dejaeger, Karel, Mues, Christophe, Vanthienen, Jan, & Baesens, Bart. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141-154.
• Setiono, R, Baesens, Bart, & Mues, Christophe. (2011). Rule extraction from minimal neural network for credit card screening. International Journal of Neural Systems, 21(4), 265-276.
• Setiono, R, Baesens, Bart, & Martens, David. (2011). Rule extraction from neural networks and support vector machines for credit scoring. Intelligent Systems Reference Library, 25, 299-320.
• Lima, Elen, Mues, Christophe, & Baesens, Bart. (2011). Monitoring and backtesting churn models. Expert Systems with Applications, 38(1), 975-982.
• Goedertier, Stijn, De Weerdt, Jochen, Martens, David, Vanthienen, Jan, & Baesens, Bart. (2011). Process discovery in event logs: An application in the telecom industry. Applied Soft Computing, 11(2), 1697-1710.
• Verbeke, Wouter, Martens, David, Mues, Christophe, & Baesens, Bart. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354-2364.
• Martens, David, Vanhoutte, Christine, De Winne, Sophie, Baesens, Bart, Sels, Luc, & Mues, Christophe. (2011). Identifying financially successful start-up profiles with data mining. Expert Systems with Applications, 38(5), 5794-5800.
• Baesens, Bart, Martens, David, Setiono, Rudy, & Zurada, Jacek M. (2011). Special Section on White Box Nonlinear Prediction Models. IEEE Transactions on Neural Networks, 22(12), 2406-2408.
• Setiono, Rudy, Baesens, Bart, & Mues, Christophe. (2011). RULE EXTRACTION FROM MINIMAL NEURAL NETWORKS FOR CREDIT CARD SCREENING. International Journal of Neural Systems, 21(4), 265-276.
• Van Gestel, T, Dewyspelaere, T, Debliquy, O, & Baesens, Bart. (2010). Modelling credit portfolios under stress. Tijdschrift Voor Bank- En Financiewezen, 7, 416-422.
• 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), IEEE Transactions on Neural Networks; 2010; Vol. 21; iss. 4.
• 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), IEEE Transactions on Autonomous Mental Development; 2010; Vol. 2; iss. 1.
• Vuylsteke, Alexander, Wen, Zhong, Baesens, Bart, & Poelmans, Jonas. (2010). Consumers' Search for Information on the Internet How and Why China Differs from Western Europe. Journal of Interactive Marketing, 24(4), 309-331.
• Van Gestel, Tony, Baesens, Bart, & Martens, David. (2010). From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing, 73(16-18), 2955-2970.
• Martens, D, Van Gestel, T, De Backer, M, Haesen, R, Vanthienen, J, & Baesens, B. (2010). Credit rating prediction using Ant Colony Optimization. Journal of the Operational Research Society, 61(4), 561-573.
• Baesens, Bart, & De Backer, Manu. (2009). Business Intelligence: New trends. Informatie, 1-10.
• Venkataraman, Girish, Rycyna, Kevin, Rabanser, Alexander, Heinze, Georg, Baesens, Bart MM, Ananthanarayanan, Vijayalakshmi, . . . Wojcik, Eva M. (2009). Morphometric Signature Differences in Nuclei of Gleason Pattern 4 Areas in Gleason 7 Prostate Cancer With Differing Primary Grades on Needle Biopsy. Journal of Urology, The, 181(1), 88-93.
• Setiono, Rudy, Baesens, Bart, & Mues, Christophe. (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.
• Glady, Nicolas, Baesens, Bart, & Croux, Christophe. (2009). Modeling churn using customer lifetime value. European Journal of Operational Research, 197(1), 402-411.
• Glady, Nicolas, Baesens, Bart, & Croux, Christophe. (2009). A modified Pareto/NBD approach for predicting customer lifetime value. Expert Systems with Applications, 36(2), 2062-2071.
• Baesens, B. (2009). Data mining: New trends, applications and challenges. Review of Business and Economics, 1, 46-61.
• Cumps, Bjorn, Martens, David, De Backer, Manu, Haesen, Raf, Viaene, Stijn, Dedene, Guido, . . . Snoeck, Monique. (2009). Inferring comprehensible business/ICT alignment rules. Information and Management, 46(2), 116-124.
• Goedertier, Stijn, Martens, David, Vanthienen, Jan, & Baesens, Bart. (2009). Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research, 10, 1305-1340.
• Wessa, Patrick, & Baesens, Bart. (2009). Explorative data mining of constructivist learning experiences and activities with multiple dimensions. Proceedings of the International Conference on Computer and Instructional Technologies, 37, 413-418.
• 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.
• Martens, David, Baesens, Bart, & Van Gestel, Tony. (2009). Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Transactions on Knowledge and Data Engineering, 21(2), 178-191.
• Baesens, Bart, & Martens, David. (2008). ICT uitdagingen in het Basel II tijdperk. Informatierecht, 32, 22-25.
• Van Laere, Elisabeth, Baesens, Bart, & Thibeault, A. (2008). Bank capital: A myth resolved. Tijdschrift Voor Bank- En Financiewezen, 1, 1-26.
• Huysmans, J, Baesens, B, & Vanthienen, J. (2008). A data miner's approach to country corruption analysis. Studies in Computational Intelligence, 135, 227-247.
• Martens, David, Bruynseels, Liesbeth, Baesens, Bart, Willekens, Marleen, & Vanthienen, Jan. (2008). Predicting going concern opinion with data mining. Decision Support Systems, 45(4), 765-777.
• Lessmann, Stefan, Baesens, Bart, Mues, Christophe, & Pietsch, Swantje. (2008). Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering, 34(4), 485-496.
• Vandecruys, Olivier, Martens, David, Baesens, Bart, Mues, Christophe, De Backer, Manu, & Haesen, Raf. (2008). Mining software repositories for comprehensible software fault prediction models. Journal of Systems and Software, 81(5), 823-839.
• Huysmans, Johan, Setiono, Rudy, Baesens, Bart, & Vanthienen, Jan. (2008). Minerva: Sequential covering for rule extraction. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 38(2), 299-309.
• Setiono, Rudy, Baesens, Bart, & Mues, Christophe. (2008). Recursive neural network rule extraction for data with mixed attributes. IEEE Transactions on Neural Networks,19(2), 299-307.
• 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.
• Huysmans, Johan, Baesens, Bart, & Vanthienen, Jan. (2007). A new approach for measuring rule set consistency. Data and Knowledge Engineering, 63(1), 167-182.
• Van Gestel, Tony, Martens, David, Baesens, Bart, FeremanS, Daniel, Huysmans, Johan, & Vanthienen, Jan. (2007). Forecasting and analyzing insurance companies' ratings. International Journal of Forecasting, 23(3), 513-529.
• Martens, David, De Backer, Manu, Haesen, Raf, Vanthienen, Jan, Snoeck, Monique, & Baesens, Bart. (2007). Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(5), 651-665.
• Baesens, Bart, Van Gestel, T, Mues, Christophe, & Vanthienen, Jan. (2006). Intelligent information systems for financial engineering. Expert Systems with Applications, 30(3), 413-414.
• Martens, D, De Backer, M, Haesen, R, Baesens, B, & Holvoet, T. (2006). Ants constructing rule-based classifiers. Studies in Computational Intelligence, 34, 21-43.
• Martens, David, De Backer, Manu, Haesen, Raf, & Baesens, Bart. (2006). Artificiële mieren en hun zoektocht naar kennis. Informatie: Maandblad Voor De Informatievoorziening, 48(4), 12-17.
• Van Gestel, Tony, Baesens, Bart, Van Dijcke, Peter, Garcia, Joao, Suykens, Johan AK, & Vanthienen, Jan. (2006). A process model to develop an internal rating system: Sovereign credit ratings. Decision Support Systems, 42(2), 1131-1151.
• Van Gestel, T, Espinoza, M, Baesens, B, Suykens, JAK, Brasseur, C, & De Moor, B. (2006). A Bayesian nonlinear support vector machine error correction model. Journal of Forecasting, 25(2), 77-100.
• Van Gestel, T, Baesens, Bart, Suykens, Johan, Van den Poel, D, Baestaens, DE, & Willekens, Marleen. (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, Baesens, B, Suykens, JAK, 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.
• Van Gestel, Tony, Baesens, Bart, Van Dijcke, P, Suykens, Johan, 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), The Journal of Credit Risk; 2005; Vol. 1; iss. 4.
• Huysmans, Johan, Baesens, Bart, Martens, David, Denys, K, & Vanthienen, Jan. (2005). New trends in data mining. Tijdschrift Voor Economie En Management, (4), 697-711.
• Baesens, Bart, Van Gestel, Tony, Stepanova, M, Vanthienen, Jan, & Van den Poel, D. (2005). Neural network survival analysis for personal loan data. Journal of the Operational Research Society, 59(9), 1089-1098.
• 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, Van Gestel, T, Stepanova, M, Van den Poel, D, & Vanthienen, J. (2005). Neural network survival analysis for personal loan data. Journal of the Operational Research Society, 56(9), 1089-1098.
• Mues, C, Baesens, B, Files, CA, & 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, 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, JAK, Baesens, B, Viaene, S, Vanthienen, J, Dedene, G, . . . Vandewalle, J. (2004). Benchmarking least squares support vector machine classifiers. Machine Learning, 54(1), 5-32.
• Van Gestel, Tony, Baesens, Bart, Garcia, J, & Van Dijcke, P. (2003). A support vector machine approach to credit scoring. Bank- En Financiewezen, 2, 73-82.
• 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.
• Viaene, Stijn, Derrig, RA, Baesens, Bart, & Dedene, Guido. (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.
• 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.
• 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.
• Souverein, M, Baesens, Bart, Viaene, Stijn, Vanderbist, Dirk, & Vanthienen, Jan. (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, JAK, Van den Poel, D, Vanthienen, J, . . . 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 Gestel, T, Suykens, JAK, Van den Poel, D, Vanthienen, J, . . . Dedene, G. (2000). Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. Lecture Notes in Artificial Intelligence, 1910, 657-664.

Conference Proceedings

• Stripling, Eugen, Baesens, B, & Vanden Broucke, S. (n.d.). Regularized Empirical EMP Maximization Framework for Profit-Driven Model Building. Not Known Yet, Not known yet.
• Höppner, S, Stripling, E, Baesens, Bart, Vanden Broucke, S, & Verdonck, T. (n.d.). Profit Driven Decision Trees for Churn Prediction. Proceedings of the Conference on Data Science, Statistics and Visualisation (DSSV 2018), Proceedings of the conference on Data Science, Statistics and Visualisation (DSSV 2018).
• Devos, A, Dhondt, J, Stripling, Eugen, Baesens, Bart, Vanden Broucke, Seppe, & Sukhatme, G. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. Proceedings of the IEEE Data Science Workshop (DSW), Proceedings of the IEEE Data Science Workshop (DSW); 2018.
• Haegemans, Tom, Reusens, Michael, Baesens, Bart, Lemahieu, Wilfried, & Snoeck, Monique. (2017). Towards a visual approach to aggregate data quality measurements. Proceedings of the International Conference on Information Quality, Proceedings of the International Conference on Information Quality; 2017.
• Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). Churn prediction using dynamic rfm-augmented node2vec. Proceedings of the Third International Workshop on Dynamics in and of Networks, ECML-PKDD 2017, 10708 LNCS, 122-138.
• Bing, Z, Vanden Broucke, Seppe, Baesens, Bart, & Maldonado, S. (2017). Improving resampling-based ensemble in Churn prediction. Proceedings of the First International Workshop on Learning With Imbalanced Domains: Theory and Applications (LIDTA 2017), Proceedings of the first international workshop on Learning With Imbalanced Domains: Theory and Applications (LIDTA 2017); 2017.
• De Koninck, Pieter, Nelissen, Klaas, Baesens, Bart, Vanden Broucke, Seppe, Snoeck, Monique, & De Weerdt, Jochen. (2017). An Approach for Incorporating Expert Knowledge in Trace Clustering. ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017), 10253, 561-576.
• Mitrovic, Sandra, Singh, Gaurav, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). Scalable rfm-enriched representation learning for churn prediction. Proceedings of the Fourth IEEE International Conference on Data Science and Advanced Analytics (DSAA2017), 2018-January, 79-88.
• Vanthienen, Jan, Baesens, Bart, Chen, Guoqing, & Wei, Qiang. (2016). Preface to the Third International Workshop on Decision Mining and Modeling for Business Processes (DeMiMoP'15). BUSINESS PROCESS MANAGEMENT WORKSHOPS, (BPM 2015),256, 402-403.
• Van Calster, Tine, Lismont, Jasmien, Oskarsdottir, María, Vanden Broucke, Seppe, Vanthienen, Jan, Lemahieu, Wilfried, & Baesens, Bart. (2016). Automated analytics: The organizational impact of analytics-as-a-service. Proceedings of the EI-KDD’16 Workshop, Proceedings of the EI-KDD’16 workshop; 2016.
• Oskarsdottir, Maria, Bravo, Cristian, Verbeke, Wouter, Sarraute, Carlos, Baesens, Bart, & Vanthienen, Jan. (2016). A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry. PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, 1151-1158.
• Zhu, Xinwei, Zhu, Guobin, Vanden Broucke, Seppe KLM, Vanthienen, Jan, & Baesens, Bart. (2015). Towards Location-Aware Process Modeling and Execution. BUSINESS PROCESS MANAGEMENT WORKSHOPS( BPM 2014), 202, 186-197.
• Dirick, Lore, Bellotti, T, Claeskens, Gerda, & Baesens, Bart. (2015). The prediction of time to default for personal loans using mixture cure models: Including macro-economic factors. Proceedings of the Credit Scoring and Credit Control XIV Conference 0.
• Pinheiro, Carlos AR, Van Vlasselaer, Veronique, Baesens, Bart, Evsukoff, Alexandre G, Silva, Moacyr AHB, & Ebecken, Nelson FF. (2015). A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data. SOFTWARE ENGINEERING IN INTELLIGENT SYSTEMS (CSOC2015), VOL 3, 349, 35-44.
• Van Vlasselaer, Veronique, Akoglu, Leman, Eliassi-Rad, Tina, Snoeck, Monique, & Baesens, Bart. (2015). Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships. 2015 48TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), 2015-March, 918-927.
• Backiel, Aimee, Verbinnen, Yannick, Baesens, Bart, & Claeskens, Gerda. (2015). Combining Local and Social Network Classifiers to Improve Churn Prediction. PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), 651-658.
• Van Vlasselaer, Veronique, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique, & Baesens, Bart. (2015). AFRAID: Fraud Detection via Active Inference in Time-evolving Social Networks. PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), 659-666.
• Gaussier, E, Cao, LB, Gallinari, P, Kwok, J, Pasi, G, & Zaiane, O. (2015). Profit Maximizing Logistic Regression Modeling for Customer Churn Prediction. PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015), 851-860.
• Backiel, Aimée, Baesens, Bart, & Claeskens, Gerda. (2014). Mining telecommunication networks to enhance customer lifetime predictions. Lecture Notes in Artificial Intelligence, 8468, 15-26.
• Vanden Broucke, Seppe KLM, Vanthienen, Jan, & Baesens, Bart. (2014). Declarative Process Discovery with Evolutionary Computing. 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2412-2419.
• Meersman, R, Panetto, H, Dillon, T, Missikoff, M, Liu, L, Pastor, O, . . . Sellis, T. (2014). Event-Based Real-Time Decomposed Conformance Analysis. ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2014 CONFERENCES, 8841, 345-363.
• Vanden Broucke, SKLM, Vanthienen, J, & Baesens, B. (2013). Third international business process intelligence challenge (BPIC'13): Volvo IT Belgium VINST. CEUR Workshop Proceedings, 1052, CEUR Workshop Proceedings; 2013; Vol. 1052.
• Vanden Broucke, Seppe, Delvaux, C, Freitas, J, Rogova, T, Vanthienen, Jan, & Baesens, Bart. (2013). Uncovering the relationship between event log characteristics and process discovery techniques. Business Process Management Workshops, 41-53.
• Van Vlasselaer, V, Meskens, J, Van Dromme, D, & Baesens, B. (2013). Using social network knowledge for detecting spider constructions in social security fraud. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, 813-820.
• De Weerdt, J, Caron, F, Vanthienen, J, & Baesens, B. (2013). Getting a grasp on clinical pathway data: An approach based on process mining. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7769 LNAI, 22-35.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2013). A new approach for variable selection in mixture cure models for prediction time of default. Proceedings of the Credit Scoring and Credit Control XIII Conference 0.
• Li, Libo, Goethals, Frank, Baesens, Bart, & Giangreco, A. (2013). Using social network data to predict technology acceptance. International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design, 5, 4623-4632.
• Seret, Alex, Vanden Broucke, Seppe, Baesens, Bart, & Vanthienen, Jan. (2013). An exploratory approach for understanding customer behavior processes bases on clustering and sequence mining. Business Process Management Workshops, 237-248.
• Vanden Broucke, Seppe, Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Validating and enhancing declarative business process models based on allowed and non-occurring past behavior. Business Process Management Workshops, 212-223.
• Louis, Philippe, & Baesens, Bart. (2013). Do for-profit micro-finance institutions achieve better financial efficiency and social impact? A generalized estimating equations panel data approach. Financial Globalisation and Sustainable Finance: Implications for Policy and Practice Conference 0.
• Li, Libo, Goethals, Frank, & Baesens, Bart. (2013). Predicting e-commerce adoption using data about product search and supplier search behavior. Crossing the Chasm of E-Business, 111-120.
• De Weerdt, Jochen, Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Getting a grasp on clinical pathway data: An approach based on process mining. Emerging Trends in Knowledge Discovery and Data Mining (LNAI 7769), 22-35.
• Vanden Broucke, Seppe, Vanthienen, Jan, & Baesens, Bart. (2013). Volvo IT Belgium VINST. Proceedings of the 3rd Business Process Intelligence Challenge Co-located with 9th International Business Process Intelligence Workshop (BPI 2013), 1052, 0.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Healthcare analytics: Examining the diagnosis-treatment cycle. CENTERIS 2013 - Conference on ENTERprise Information Systems / HCIST 2013 - International Conference on Health and Social Care Information Systems and Technologies, 9, 996-1004.
• Bagheri, E, Gasevic, D, Halle, S, Hatala, M, Nezhad, HRM, & Reichert, M. (2013). Business rule patterns and their application to process analytics. 17TH IEEE INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS (EDOCW 2013), 13-20.
• Vanden Broucke, Seppe KLM, De Weerdt, Jochen, Vanthienen, Jan, & Baesens, Bart. (2013). A Comprehensive Benchmarking Framework (CoBeFra) for Conformance Analysis between Procedural Process Models and Event Logs in ProM. 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM),254-261.
• Van Vlasselaer, Veronique, Meskens, Jan, Van Dromme, Dries, & Baesens, Bart. (2013). Using Social Network Knowledge for Detecting Spider Constructions in Social Security Fraud. 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 819-826.
• 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. 18th Americas Conference on Information Systems 2012, AMCIS 2012, 1, 50-60.
• Caron, Filip, Vanden Broucke, Seppe, Vanthienen, Jan, & Baesens, Bart. (2012). On the distinction between truthful, invisible, false and unobserved events. Proceedings of the 18th Americas Conference on Information Systems, Proceedings of the 18th Americas Conference on Information Systems; 2012.
• Verbraken, T, Verbeke, W, Weber, R, Bravo, C, & Baesens, B. (2012). A profit based performance measure for consumer credit scoring models. Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012, 225-227.
• De Weerdt, Jochen, Vanden Broucke, Seppe, Vanthienen, Jan, & Baesens, Bart. (2012). Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. Evolutionary Computation (CEC), 2012 IEEE Congress on, 1-8.
• Vanden Broucke, Seppe, De Weerdt, Jochen, Baesens, Bart, & Vanthienen, Jan. (2012). An improved artificial negative event generator to enhance process event logs. Lecture Notes in Computer Science, 7328 LNCS, 254-269.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2012). Rule-Based Business Process Mining: Applications for Management. MANAGEMENT INTELLIGENT SYSTEMS, 171, 273-282.
• De Weerdt, Jochen, Vanden Broucke, Seppe KLM, Vanthienen, Jan, & Baesens, Bart. (2012). Leveraging Process Discovery with Trace Clustering and Text Mining for Intelligent Analysis of Incident Management Processes. 2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC); 2012; pp.
• Moges, Helen Tadesse, Lemahieu, Wilfried, & Baesens, Bart. (2012). The use of data quality information (DQI) for decision-making: An exploratory study. Proceedings of the International Conference on Business Management and Information Systems (ICBMIS 2012), 386-394.
• Caron, Filip, Vanthienen, Jan, De Weerdt, Jochen, & Baesens, Bart. (2012). Advanced Care-Flow Mining and Analysis. BUSINESS PROCESS MANAGEMENT WORKSHOPS, PT I, 99(PART 1), 167-168.
• Verbraken, Thomas, Goethals, Frank, Verbeke, Wouter, & Baesens, Bart. (2012). Using Social Network Classifiers for Predicting E-Commerce Adoption. E-LIFE: WEB-ENABLED CONVERGENCE OF COMMERCE, WORK, AND SOCIAL LIFE, 108, 9-21.
• Louis, Philippe, Van Laere, Elisabeth, & Baesens, Bart. (2011). Predicting bank rating transitions using optimal competing risks survival analysis models. Proceedings of the Credit Scoring and Credit Control XII Conference, Proceedings of the credit scoring and credit control XII conference; 2011.
• De Weerdt, Jochen, De Backer, Manu, Vanthienen, Jan, & Baesens, Bart. (2011). A Critical Evaluation Study of Model-Log Metrics in Process Discovery. BUSINESS PROCESS MANAGEMENT WORKSHOPS, 66, 158-169.
• Louis, Philippe, Van Laere, Elisabeth, & Baesens, Bart. (2011). Motivating and predicting bank rating transitions using optimal survival analysis models. Proceedings of the 24th Australasian Finance Banking Conference, Proceedings of the 24th Australasian Finance Banking Conference; 2011.
• De Weerdt, Jochen, De Backer, Manu, Vanthienen, Jan, & Baesens, Bart. (2011). A robust F-measure for evaluating discovered process models. CIDM, 148-155.
• Moges, Helen Tadesse, Dejaeger, Karel, Lemahieu, Wilfried, & Baesens, Bart. (2011). Data quality for credit risk management: New insights and challenges. ICIQ 2011 - Proceedings of the 16th International Conference on Information Quality, 632-646.
• Dejaeger, Karel, Hamers, Bart, Poelmans, Jonas, & Baesens, Bart. (2010). A novel approach to the evaluation and improvement of data quality in the financial sector. Proceedings of 15th International Conference on Information Quality (ICIQ 2010),Proceedings of 15th International Conference on Information Quality (ICIQ 2010); 2010.
• Castermans, G, Martens, D, Van Gestel, T, Hamers, B, & Baesens, B. (2010). An overview and framework for PD backtesting and benchmarking. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 61(3), 359-373.
• Vanhoutte, Christine, Martens, David, De Winne, Sophie, Sels, Luc, & Baesens, Bart. (2010). The initial resource-performance relationship in new ventures: Towards a configurational approach. Proceedings of the 7th International AGSE Entrepreneurship Research Exchange (CD-rom), 147-161.
• Ma, Baojun, Dejaeger, Karel, Vanthienen, Jan, & Baesens, Bart. (2010). Software Defect Prediction Based on Association Rule Classification. ELECTRONIC-BUSINESS INTELLIGENCE: FOR CORPORATE COMPETITIVE ADVANTAGES IN THE AGE OF EMERGING TECHNOLOGIES GLOBALIZATION, 14, 396.
• Verbeke, Wouter, Baesens, Bart, Martens, David, De Backer, Manu, & Haesen, Raf. (2009). Including domain knowledge in customer churn prediction using AntMiner. Advances in Data Mining in Marketing, 10-21.
• Burgin, Mark, Chowdhury, Masud H, Ham, Chan H, Ludwig, Simone, Su, Weilian, & Yenduri, Sumanth. (2009). Fraud detection in statistics education based on the compendium and reproducible computing. IEEE Proceedings of the World Congress on Computer Science and Information Engineering, 50-54.
• Wessa, Patrick, & Baesens, Bart. (2009). Fraud detection in statistics education based on the compendium platform and reproducible computing. IEEE Proceedings of the World Congress on Computer Science and Information Engineering, 3, 50-54.
• Baesens, B, Mues, C, Martens, D, & Vanthienen, J. (2008). 50 years of data mining and OR: Upcoming trends and challenges. 50th Annual Conference of the Operational Research Society 2008, OR50, 67-79.
• Goedertier, Stijn, Martens, David, Baesens, Bart, Haesen, Raf, & Vanthienen, Jan. (2008). Process mining as first-order classification learning on logs with negative events. BUSINESS PROCESS MANAGEMENT WORKSHOPS, 4928, 42-53.
• Baesens, Bart, Setiono, Rudy, & Mues, C. (2007). Neural network rule extraction and decision tables for software fault prediction. Lecture Notes in Computer Science, Lecture Notes in Computer Science; 2007.
• Vanthienen, Jan, Martens, David, Goedertier, Stijn, & Baesens, Bart. (2007). Placing process intelligence within the business intelligence framework. PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON INFORMATION AND MANAGEMENT SCIENCES, 6, 218-223.
• Setiono, R, Baesens, Bart, & Mues, Christophe. (2006). Risk management and regulatory compliance: A data mining framework based on neural network rule extraction. Proceedings of the International Conference on Information Systems (ICIS 2006), 71-85.
• Mues, Christophe, Huysmans, Johan, Vanthienen, Jan, & Baesens, Bart. (2006). Comprehensible credit-scoring knowledge visualization using decision tables and diagrams. ENTERPRISE INFORMATION SYSTEMS VI, 6, 109.
• Chen, H, Wang, FY, Yang, CC, Zeng, D, Chau, M, & Chang, K. (2006). Country corruption analysis with self organizing maps and support vector machines. INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS, 3917, 103-114.
• Huysmans, Johan, Baesens, Bart, & Vanthienen, Jan. (2006). ITER: An algorithm for predictive regression rule extraction. DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 4081, 270-279.
• Martens, David, De Backer, Manu, Haesen, Raf, Baesens, Bart, Mues, Christophe, & Vanthienen, Jan. (2006). Ant-based approach to the knowledge fusion problem. ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 4150, 84-95.
• Van Gestel, Tony, Suykens, JAK, Pelckmans, Kristiaan, & Baesens, Bart. (2005). Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines. Workshop on Machine Learning in Finance, NIPS 2005 Conference, Workshop on Machine Learning in Finance, NIPS 2005 Conference; 2005.
• Althoff, KD, Dengel, A, Bergmann, R, Nick, M, & RothBerghofer, T. (2005). From knowledge discovery to implementation: A business intelligence approach using neural network rule extraction and decision tables. PROFESSIONAL KNOWLEDGE MANAGEMENT, 3782, 483-495.
• Egmont-Petersen, M, Feelders, A, & Baesens, B. (2005). Confidence intervals for probabilistic network classifiers. COMPUTATIONAL STATISTICS & DATA ANALYSIS,49(4), 998-1019.
• Huysmans, J, Baesens, B, & Vanthienen, J. (2005). A comprehensible SOM-based scoring system. MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, PROCEEDINDS, 3587, 80-89.
• De Backer, M, Haesen, R, Martens, D, & Baesens, B. (2005). A stigmergy based approach to data mining. AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 3809, 975-978.
• Meeus, N, Huysmans, J, Baesens, B, Vanthienen, J, & Vandebroek, M. (2005). The use of knowledge discovery techniques for behavioural scoring. Data Mining VI: Data Mining, Text Mining and Their Business Applications, 361-370.
• Mues, C, Baesens, B, Files, CM, & Vanthienen, J. (2004). Decision diagrams in machine learning: An empirical study on real-life credit-risk data. DIAGRAMMATIC REPRESENTATION AND INFERENCE, 2980, 395-397.
• Mues, Christophe, Baesens, Bart, Huysmans, Johan, & Vanthienen, Jan. (2004). Comprehensible credit-scoring knowledge visualization using decision tables and diagrams. Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS2004), 226-232.
• Huysmans, Johan, Baesens, Bart, Mues, Christophe, & Vanthienen, Jan. (2004). Web usage mining with time constrained association rules. Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS2004), 343-348.
• Huysmans, J, Baesens, B, & Vanthienen, J. (2004). The influence of caching on web usage mining. DATA MINING V, 10, 77-86.
• Baesens, B, Mues, C, De Backer, M, Vanthienen, J, & Setiono, R. (2003). Building intelligent credit scoring systems using decision tables. ICEIS 2003 - Proceedings of the 5th International Conference on Enterprise Information Systems, 2, 19-25.
• Baesens, Bart, Mues, Christophe, Setiono, R, De Backer, Manu, & Vanthienen, Jan. (2003). Building intelligent credit scoring systems using decision tables- Best paper nomination. Proceedings of the Fifth International Conference on Enterprise Information Systems (ECEIS'2003), 19-25.
• Van Gestel, T, Baesens, B, Suykens, J, Espinoza, M, Baestaens, DE, Vanthienen, J, & De Moor, B. (2003). Bankruptcy prediction with Least Squares Support Vector Machine Classifiers. 2003 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING, PROCEEDINGS, 2003-January, 1-8.
• 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. ICEIS 2002 - Proceedings of the 4th International Conference on Enterprise Information Systems, 1, 446-454.
• Buckinx, W, Baesens, B, Van den Poel, D, Van Kenhove, P, & Vanthienen, J. (2002). Using machine learning techniques to predict defection of top clients. DATA MINING III,6, 509-517.
• 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), 373-421.
• Baesens, B, Egmont-Petersen, M, Castelo, R, & Vanthienen, J. (2002). Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search. 16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL III, PROCEEDINGS, 16(3), 49-52.
• Verstraeten, G, Baesens, Bart, Van den Poel, D, Egmont-Petersen, M, Van Kenhove, P, & Vanthienen, Jan. (2002). Targeting long-life customers: Towards a segmented CRM approach. Proceedings of the Thirty-First European Marketing Academy Conference (EMAC'2002) on 'Marketing in a Changing World: Scope, Opportunities and Challenges', 2321-2324.
• Baesens, Bart, Setiono, R, Mues, Christophe, Viaene, Stijn, & Vanthienen, Jan. (2001). Building credit-risk evaluation expert systems using neural network rule extraction and decision tables. Proceedings of the Twenty Second International Conference on Information Systems (ICIS), 159-168.
• Viaene, Stijn, Baesens, Bart, Van den Poel, D, Vanthienen, Jan, & Dedene, Guido. (2001). The Bayesian evidence framework for database marketing modeling using both RFM and non-RFM predictors. World Multiconference on Systemics, Cybernetics and Informatics, World Multiconference on Systemics, Cybernetics and Informatics; 2001.
• Viaene, S, Baesens, B, Van den Poel, D, Dedene, G, Vandenbulcke, J, & Vanthienen, J. (2000). Wrapped feature selection for binary classification Bayesian regularisation neural networks: A database marketing application. DATA MINING II, 2, 353-362.
• Viaene, Stijn, Baesens, Bart, Van Gestel, Tony, Suykens, Johan, Dedene, D, De Moor, Bart, & Vanthienen, Jan. (2000). Least squares support vector machine classifiers : An empirical evaluation. Proceedings of the 12th Belgian-Dutch Artificial Intelligence Conference (BNAIC), Proceedings of the 12th Belgian-Dutch Artificial Intelligence Conference (BNAIC); 2000.
• Viaene, Stijn, Baesens, Bart, Dedene, Guido, Vanthienen, Jan, & Vandenbulcke, Jacques. (2000). Sensitivity based pruning of input variables by means of weight cascaded retraining.
• Sanfeliu, A, Villanueva, JJ, Vanrell, M, Alquezar, R, Jain, AK, & Kittler, J. (2000). Wrapped feature selection by means of guided neural network optimisation. 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 15(2), 113-116.
• Baesens, B, Viaene, S, Van Gestel, T, Suykens, JAK, Dedene, G, De Moor, B, & Vanthienen, J. (2000). An empirical assessment of kernel type performance for least squares Support Vector Machine classifiers. KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS, 1, 313-316.

Abstracts, Presentations, Posters

• Oskarsdottir, María, Van Calster, Tine, Baesens, Bart, Lemahieu, Wilfried, & Vanthienen, Jan. (2018). A representation of dynamic networks for early churn detection in telco.
• Oskarsdottir, María, Bravo, Cristián, Verbeke, Wouter, Baesens, Bart, & Vanthienen, Jan. (2018). Effects of network architecture on model performance when predicting churn in telco.
• Oskarsdottir, María, Bravo, Cristián, Baesens, Bart, & Vanthienen, Jan. (2018). Social network analytics in micro-lending.
• Lismont, Jasmien, Ram, Sudha, Baesens, Bart, Lemahieu, Wilfried, & Vanthienen, Jan. (2018). The (pseudo-)social behavior of products in offline retail stores: Predicting increase in product interpurchase time.
• Reusens, Michael, Baesens, Bart, Lemahieu, Wilfried, & Sels, Luc. (2018). Identifying successful search patterns for improved job recommendation.
• Van Calster, Tine, Reusens, Michael, Baesens, Bart, & Lemahieu, Wilfried. (2018). Forecasting blood donations with neural networks.
• Stripling, Eugen, Baesens, Bart, & Vanden Broucke, Seppe. (2018). Building profit-sensitive classifiers for maximum profit.
• Oskarsdottir, María, Bravo, Cristián, Vanthienen, Jan, & Baesens, Bart. (2018). Behavior based time-to-default predictions.
• Mitrovic, Sandra, Baesens, Bart, Lemahieu, Wilfried, & De Weerdt, Jochen. (2017). On added value of feature engineering for churn prediction.
• Oskarsdottir, María, Van Calster, Tine, Lemahieu, Wilfried, Baesens, Bart, & Vanthienen, Jan. (2017). Clustering time series from call networks to predict churn.
• Oskarsdottir, María, Bravo, Cristián, Verbeke, Wouter, Sarraute, Carlos, Baesens, Bart, & Vanthienen, Jan. (2017). Churn prediction in the telecommunication industry using social network analytics.
• Van Calster, Tine, Reusens, Michael, Oskarsdottir, María, Mitrovic, Sandra, Lismont, Jasmien, De Weerdt, Jochen, . . . Vanthienen, Jan. (2017). What's in the network? A stepwise overview of working with networked data in R.
• Haupt, J, Stripling, Eugen, Baesens, Bart, Vanden Broucke, Seppe, & Lessmann, S. (2017). Profit-maximizing scorecard development.
• Oskarsdottir, María, Bravo, Cristián, Verbeke, Wouter, Sarraute, Carlos, Baesens, Bart, & Vanthienen, Jan. (2017). Profit driven comparison of social network analytics methods for predicting customer churn in telco.
• Lismont, Jasmien, Baesens, Bart, Lemahieu, Wilfried, & Vanthienen, Jan. (2017). Discovering communities in customer purchase behavior by means of social network analytics.
• Van Calster, Tine, Lemahieu, Wilfried, & Baesens, Bart. (2016). Data-driven algorithm for bottom-up hierarchical forecasting: A global sales forecast for the Coca-Cola Company.
• Van Calster, Tine, Reusens, Michael, Baesens, Bart, & Lemahieu, Wilfried. (2016). Forecasting blood donations with Google Trends.
• Oskarsdottir, María, Vanthienen, Jan, Baesens, Bart, Van Vlasselaer, Véronique, & Backiel, Aimée. (2015). Effects of community-based churn detection in the telecom sector.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2015). Credit risk modeling using mixture cure models: Variable selection and time-dependent covariates.
• Lismont, Jasmien, Vanthienen, Jan, Baesens, Bart, & Lemahieu, Wilfried. (2015). The role of the data scientist in the modern organization.
• Van Calster, Tine, Lemahieu, Wilfried, & Baesens, Bart. (2015). Hierarchical sales forecasting for The Coca-Cola Company - A time series benchmark and initial tool optimization.
• Reusens, Michael, Seret, Alex, Baesens, Bart, Lemahieu, Wilfried, & Sels, Luc. (2015). Application of a personalized collaborative filtering job recommender system for the Flemish employment service.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2015). Advances on the use of mixture cure models in the credit risk context.
• Van Vlasselaer, Véronique, Akoglu, Leman, Eliassi-Rad, Tina, Snoeck, Monique, & Baesens, Bart. (2014). Gotch’all! Advanced network analysis for detecting groups of fraud.
• Dirick, Lore, Claeskens, Gerda, Vasnev, A, & Baesens, Bart. (2014). Using mixture cure models with unobserved heterogeneity for the analysis of credit loan data.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2014). Modeling business decisions and processes – which comes first?
• Dirick, Lore, Claeskens, Gerda, Vasnev, A, & Baesens, Bart. (2014). Modeling unobserved heterogeneity in mixture cure models.
• Van Vlasselaer, Véronique, Akoglu, Leman, Eliassi-Rad, Tina, Snoeck, Monique, & Baesens, Bart. (2014). Finding cliques in large fraudulent networks: Theory and insights.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2013). The analysis of credit risk data: An information criterion for multiple event mixture cure models.
• Van Vlasselaer, Véronique, Van Dromme, Dries, & Baesens, Bart. (2013). Social network analysis for detecting spider constructions in social security fraud: New insights and challenges.
• Baesens, Bart, & Van Vlasselaer, Véronique. (2013). Social network analytics for fraud detection: Insights and challenges.
• Van Vlasselaer, Véronique, & Baesens, Bart. (2013). Improving fraud detection techniques using social network analytics for the Belgian government.
• Seret, Alex, Maldonado, Sebastian, Weber, Richard, & Baesens, Bart. (2013). Knowledge based feature selection for unsupervised learning: Theory and application.
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2013). Performing model selection in mixture cure models for the analysis of credit risk data.
• Louis, Philippe, & Baesens, Bart. (2013). Do for-profit micro-finance institutions achieve better financial efficiency and social impact?
• Vantieghem, J, Van Laere, Elisabeth, & Baesens, Bart. (2013). The difference between Moody's and S bank ratings: Is discretion in the rating process causing a split?
• Dirick, Lore, Claeskens, Gerda, & Baesens, Bart. (2013). The analysis of credit risk data: Variable selection for a mixture cure model.
• Dejaeger, Karel, Verbraken, Thomas, & Baesens, Bart. (2012). Assessing Bayesian network classifiers for software defect prediction.
• Verbraken, Thomas, Lessmann, S, & Baesens, Bart. (2012). Toward profit-driven churn modeling with predictive marketing analytics.
• Verbeke, Wouter, Dejaeger, Karel, Verbraken, Thomas, Martens, David, & Baesens, Bart. (2011). Mining social networks for customer churn prediction.
• Verbeke, Wouter, Verbraken, Thomas, Martens, David, & Baesens, Bart. (2011). Relational learning for customer churn prediction: The complementarity of networked and non-networked classifiers.
• Van Laere, Elisabeth, & Baesens, Bart. (2011). Analyzing bank ratings: Key determinants and procyclicality.
• Dejaeger, Karel, Verbeke, Wouter, Huysmans, Johan, Mues, Christophe, Vanthienen, Jan, & Baesens, Bart. (2010). Rule based predictive models, decision table and tree: An empirical evaluation on comprehensibility.
• Verbeke, Wouter, Dejaeger, Karel, Martens, David, & Baesens, Bart. (2010). Customer churn prediction: Does technique matter?
• Verbeke, Wouter, Berteloot, K, Castermans, G, Martens, David, Van Gestel, T, & Baesens, Bart. (2010). Modeling credit rating migrations dependent on the business cycle.
• Setiono, Rudy, Dejaeger, Karel, Verbeke, Wouter, Martens, David, & Baesens, Bart. (2010). Software effort prediction using regression rule extraction from neural networks.
• Verbeke, Wouter, Dejaeger, Karel, & Baesens, Bart. (2010). Comparing classification techniques to forecast customer churn.
• Baojun, Ma, Dejaeger, Karel, Vanthienen, Jan, & Baesens, Bart. (2010). Software defect prediction based on association rule classification.
• Van Laere, Elisabeth, & Baesens, Bart. (2010). The development of a simple and intuitive rating system under solvency II.
• Wessa, Patrick, Baesens, Bart, Poelmans, Stephan, & Van Stee, Ed. (2009). Reproducible computing workshop.
• Van Gool, Joris, Baesens, Bart, Sercu, Piet, & Verbeke, Wouter. (2009). An analysis of the applicability of credit scoring for microfinance.
• Verbeke, Wouter, Martens, David, & Baesens, Bart. (2009). Building comprehensible customer churn prediction models with advanced rule induction techniques.
• Loterman, G, Brown, I, Martens, David, Mues, Christophe, & Baesens, Bart. (2009). Benchmarking state-of-the-art regression algorithms for loss given default modelling.
• Vuylsteke, Alexander, Wen, Zhong, Baesens, Bart, & Poelmans, Jonas. (2009). Consumers online information search: A cross-cultural study between China and Western Europe.
• Verbeke, Wouter, Baesens, Bart, Martens, David, De Backer, Manu, & Haesen, Raf. (2009). Building accurate, comprehensible, and justifiable customer churn prediction models using AntMiner. American Statistical Association.
• Martens, David, Van Gestel, T, Vanden Branden, K, Jacobs, J, & Baesens, Bart. (2009). A practical framework for credit risk stress testing.
• Van Laere, Elisabeth, & Baesens, Bart. (2009). Regulatory and economic capital: Theory and practice, evidence from the field.
• Vanhoutte, Christine, Martens, David, Sels, Luc, Maes, Johan, & Baesens, Bart. (2008). Resource configurations for top performing start-ups: An exploratory study with classification trees. The University of North Carolina.
• Castermans, G, Martens, David, Van Gestel, T, Hamers, B, & Baesens, Bart. (2007). Quantitative Validation: An Overview and Framework for PD Backtesting and Benchmarking.
• Martens, David, Vanthienen, Jan, Goedertier, Stijn, & Baesens, Bart. (2007). Placing process intelligence within the business intelligence framework.
• Martens, David, Vanthienen, Jan, Baesens, Bart, & Mues, Christophe. (2006). Measuring the consistency with prior knowledge of classification models.
• Martens, David, Baesens, Bart, Mues, Christophe, Hsin-Vonn, Seow, Shahi, Reza, & Vanthienen, Jan. (2006). Building acceptable classifiers with ants.
• Huysmans, Johan, Baesens, Bart, & Vanthienen, Jan. (2005). Country corruption analysis with SOMs and SVMs.
• Martens, David, De Backer, Manu, Haesen, Raf, & Baesens, Bart. (2005). On the use of ant systems for data mining.
• Martens, David, Baesens, Bart, Van Gestel, Tony, & Vanthienen, Jan. (2005). Benchmarking state-of-the-art classification techniques for credit scoring.
• Martens, David, Baesens, Bart, Van Gestel, Tony, & Vanthienen, Jan. (2005). Adding comprehensibility to support vector machine models using rule extraction techniques (poster). The University of Edinburgh Management School.
• Baesens, Bart, Van De Walle, Patricia, Callewaert, Barbara, Huenaerts, Catherine, Molenaers, Guy, Meeusen, Caroline, . . . Desloovere, Kaat. (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.
• Huysmans, Johan, Baesens, Bart, & Vanthienen, Jan. (2004). Web usage mining: A practical study.
• Viaene, Stijn, Derrig, R, Baesens, Bart, & Dedene, Guido. (2002). New developments in insurance fraud detection modeling: A comparison of state- of- the- art classification techniques for expert automobile insurance fraud detection. The Wharton School, Penn State University.
• Baesens, Bart, Viaene, Stijn, & Vanthienen, Jan. (2001). A comparative study of state of the art classification algorithms for credit scoring.
• Baesens, Bart, Setiono, R, De Lille, V, Viaene, Stijn, & Vanthienen, Jan. (2001). Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.
• Viaene, Stijn, Derrig, R, Baesens, Bart, & Dedene, Guido. (2001). A comparison of state-of-the-art classification techniques for expert automobile insurance fraud detection.

Books

• Lemahieu, Wilfried, Vanden Broucke, Seppe, & Baesens, Bart. (2018). Principles of database management – the practical guide to storing, managing and analyzing small and big data. Cambridge University Press.
• Verbeke, W, Bravo, C, & Baesens, Bart. (2017). Profit driven business analytics - a practitioner’s guide to transforming big data into added value. Wiley.
• Scheule, H, Roesch, D, & Baesens, Bart. (2017). Credit risk analytics: The R companion. CreateSpace Amazon.
• Vanden Broucke, Seppe, & Baesens, Bart. (2017). Web scraping for data science with Python. CreateSpace Amazon.
• Baesens, Bart, Roesch, D, & Scheule, H. (2016). Credit Risk Analytics – Measurement Techniques, Applications and Examples in SAS. Wiley.
• Baesens, B. (2016). 大数据分析-数据科学应用场景与实践精髓. Wiley.
• Baesens, Bart, Van Vlasselaer, Véronique, & Verbeke, Wouter. (2015). Fraud Analytics using Descriptive, Predictive & Social Network Techniques (1st ed.). Wiley.
• Baesens, Bart, Backiel, Aimée, & Vanden Broucke, Seppe. (2015). Beginning Java programming: The object-oriented approach (1st ed.). Wrox.
• 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) (Vol. 202).
• Baesens, B. (2014). Analytics in a Big Data World (1st ed.). Wiley.
• Van Gestel, T, & Baesens, Bart. (2009). Credit Risk Management: Basic concepts: Financial risk components, rating analysis, models, economic and regulatory capital(Vol. 9780199545117). Oxford University Press.

Book Chapters

• Verbraken, Thomas, Van Vlasselaer, Véronique, Verbeke, Wouter, Martens, David, & Baesens, Bart. (2013). Advanced rule base learning: Active learning, rule extraction, and incorporating domain knowledge. In Advanced database marketing: Innovative methodologies applications for managing customer relationships (p. Advanced database marketing: innovative methodologies applications for managing customer relationships; 2013). Gower Publishing; London (UK).
• Stahlbock, R, Crone, SF, & Lessmann, S. (2010). Building Acceptable Classification Models. In (Vol. 8, pp. 53-74). Springer.
• Martens, David, Huysmans, Johan, Setiono, Rudy, Vanthienen, Jan, & Baesens, Bart. (2008). Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. In (Vol. 80, pp. 33-63). Springer Verlag.
• Martens, David, Huysmans, Johan, Setiono, Rudy, Vanthienen, Jan, & Baesens, Bart. (2008). An overview of issues and application in credit scoring. In Rule extraction from support vector machines, studies in computational intelligence (pp. 33-63). Springer.
• Baesens, Bart, Mues, Christophe, Setiono, R, De Backer, Manu, & Vanthienen, Jan. (2003). Building intelligent credit scoring systems using decision tables. In Enterprise information systems V (pp. 131-137). Kluwer.
• Baesens, Bart, Setiono, R, Mues, Christophe, Viaene, Stijn, & Vanthienen, Jan. (2001). Building intelligent credit-risk evaluation expert systems using neural network rule extraction and decision tables. In New directions in software engineering (pp. 121-133). Leuven University Press; Leuven (Belgium).

PhD Dissertation

• Van Calster, T. (2018). A matter of time - leveraging time series data for business applications.
• Reusens, M. (2018). Towards a better understanding of recommender system in the labor market.
• Lismont, J. (2018). From bit to business: Addressing managerial and practical challenges of analytics adoption.
• Li, L. (2017). Essays on relational and time to event analysis.
• Seret, A. (2015). Business-driven data mining: New algorithms and applications.
• Van Vlasselaer, V. (2015). FAIR: Forecasting and network analytics for collection risk management.
• Dirick, L. (2015). Contributions to the analysis of credit risk data using advanced survival analysis techniques.
• Vanden Broucke, S. (2014). Advances in Process Mining: Artificial negative events and othertechniques.
• Moges, H. (2014). A contextual data quality analysis for credit risk management in financial institutions.
• Louis, P. (2013). Case Studies in Quantitative Financial Modeling.
• Verbraken, T. (2013). Business oriented data analytics: Theory and case studies.
• Caron, F. (2013). Business process analytics for enterprise risk management and auditing.
• Verbeke, W. (2012). Profit driven data mining in massive customer networks: New insights and algorithms.
• De Weerdt, J. (2012). Business process discovery: New techniques and applications.
• Dejaeger, K. (2012). Essays on empirical software engineering.
• Van Laere, E. (2011). Capital regulation of financial institutions, the role of ratings and the tension field between regulation and economic reality.
• Martens, D. (2008). Building acceptable classification models for financial engineering applications.
• Glady, N. (2008). Customer profitability modeling.
• Baesens, B. (2003). Developing intelligent systems for credit scoring using machine learning techniques.

Reports

• Reusens, Michael, Haegemans, Tom, Lemahieu, Wilfried, Snoeck, Monique, Baesens, Bart, & Sels, Luc. (2017). Understanding recommendation quality using embeddings. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Dirick, Lore, Bellotti, Tony, Claeskens, Gerda, & Baesens, Bart. (2016). Macro-economic factors in credit risk calculations: Including time-varying covariates in mixture cure models. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Vanden Broucke, Seppe, Vanthienen, Jan, & Baesens, Bart. (2014). Straightforward Petri net-based event log generation in ProM. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Vanden Broucke, Seppe, Muñoz-Gama, J, Carmona, J, Baesens, Bart, & Vanthienen, Jan. (2013). Event-based real-time decomposed conformance analysis. Polytechnic University of Catalonia, Department of Information Languages and Systems.
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2013). Advances in rule-based process mining: Applications for enterprise risk management and auditing. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Vanden Broucke, Seppe, De Weerdt, Jochen, Vanthienen, Jan, & Baesens, Bart. (2013). On replaying process execution traces containing positive and negative events. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Caron, Filip, Vanthienen, Jan, & Baesens, Bart. (2012). A comprehensive framework for the application of process mining in risk management and compliance checking. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Vanden Broucke, Seppe, De Weerdt, Jochen, Vanthienen, Jan, & Baesens, Bart. (2012). An improved process event log artificial negative event generator. KU Leuven - Faculty of Economics and Business; Leuven (Belgium).
• Magerman, Tom, Van Looy, Bart, Baesens, Bart, & Debackere, Koenraad. (2011). Assessment of Latent Semantic Analysis (LSA) text mining algorithms for large scale mapping of patent and scientific publication documents. K.U.Leuven - Faculty of Business and Economics; Leuven (Belgium).
• Huysmans, Johan, Setiono, Rudy, Baesens, Bart, & Vanthienen, Jan. (2007). A new approach for the extraction of knowledge from opaque predictive models. K.U.Leuven - Faculty of Economics and Applied Economics.
• Goedertier, Stijn, Martens, David, Baesens, Bart, Haesen, Raf, & Vanthienen, Jan. (2007). A new approach for discovering business process models from event logs. K.U.Leuven - Faculty of Economics and Applied Economics.
• Cumps, Bjorn, Martens, David, De Backer, Manu, Haesen, Raf, Viaene, Stijn, Dedene, Guido, . . . Snoeck, Monique. (2007). Predicting business/ICT alignment with AntMiner. K.U.Leuven - Faculty of Economics and Applied Economics.
• Glady, N, Baesens, Bart, & Croux, Christophe. (2006). Modeling customer loyalty using customer lifetime value. K.U.Leuven - Faculty of Economics and Applied Economics.
• Huysmans, Johan, Baesens, Bart, & Vanthienen, Jan. (2006). Using rule extraction to improve the comprehensibility of predictive models. K.U.Leuven - Faculty of Economics and Applied Economics.
• Martens, David, Baesens, Bart, Van Gestel, Tony, & Vanthienen, Jan. (2005). Comprehensible credit scoring models using rule extraction from support vector machines. K.U.Leuven - Departement toegepaste economische wetenschappen.
• Van Gestel, Tony, Baesens, Bart, Van Dijcke, P, Garcia, J, Suykens, JAK, & Vanthienen, Jan. (2004). A process model to develop an internal rating system: Sovereign credit ratings. ESAT-SISTA, K.U.Leuven; Leuven (Belgium).
• Van Gestel, Tony, Espinoza, M, Baesens, Bart, Suykens, Johan, Brasseur, C, & De Moor, Bart. (2004). A Bayesian nonlinear support vector machine error correction model. ESAT-SISTA, K.U.Leuven; Leuven (Belgium).
• Mues, Christophe, Baesens, Bart, Files, CM, & Vanthienen, Jan. (2004). Decision diagrams in machine learning: An empirical study on real-life credit-risk data. K.U.Leuven - Departement toegepaste economische wetenschappen.
• Van Gestel, Tony, Baesens, Bart, Suykens, JAK, Van den Poel, D, Baestaens, DE, & Willekens, Marleen. (2004). Bayesian Kernel-based classification for financial distress detection. Department of Marketing, Ghent University; Gent.
• Baesens, Bart, Verstraeten, G, Van den Poel, D, Egmont-Petersen, M, Van Kenhove, P, & Vanthienen, Jan. (2002). Bayesian network classifiers for identifying the slope of customer-lifecycle of long-life customers. Department of Marketing, Ghent University; Gent.
• Viaene, Stijn, Baesens, Bart, Van den Poel, D, Vanthienen, Jan, & Dedene, Guido. (2001). Bayesian neural network learning for repeat purchase modelling in direct marketing. K.U.Leuven - Departement toegepaste economische wetenschappen.
• Baesens, Bart, Viaene, Stijn, & Vanthienen, Jan. (2000). Post-processing of association rules. K.U.Leuven; Leuven.
• Viaene, Stijn, Baesens, Bart, Van den Poel, D, Dedene, Guido, & Vanthienen, Jan. (2000). Wrapped feature selection for neural networks in direct marketing. K.U.Leuven; Leuven.
• Baesens, Bart, Viaene, Stijn, Van Gestel, Tony, Suykens, Johan, Dedene, Guido, De Moor, Bart, & Vanthienen, Jan. (2000). Least squares support vector machine classifiers: An empirical evaluation. K.U.Leuven - Departement toegepaste economische wetenschappen.
• Viaene, Stijn, Baesens, Bart, Dedene, Guido, Vanthienen, Jan, & Vandenbulcke, Jacques. (1999). Sensivity based pruning of input variables by means of weight cascaded retraining. K.U.Leuven - Departement toegepaste economische wetenschappen.

Science Outreach

• Lismont, Jasmien, Van Calster, Tine, Oskarsdottir, María, Vanthienen, Jan, Baesens, Bart, & Lemahieu, Wilfried. (2015). API for prediction and machine learning: Poll results and analysis. Gregory Piatetsky-Shapiro.
• Baesens, Bart, Backiel, Aimée, & Vanden Broucke, Seppe. (2015). The state of database access in Java: Passchendaele revisited. Cutter Consortium.
• Vanden Broucke, Seppe, Baesens, Bart, & Vanthienen, Jan. (2013). Closing the loop: State of the art in business process analytics. Cutter Consortium.
• Dejaeger, Karel, Vanden Broucke, Seppe, Eerola, Tuomas, Wehkamp, Rainer, Goedhuys, Lieve, Riis, Mikkel, & Baesens, Bart. (2012). Beyond the hype: Cloud computing in analytics. Techila Technologies.

Other

• Dejaeger, Karel, Verbeke, Wouter, Martens, David, & Baesens, Bart. (2010). Het voorspellen van software-ontwikkelkosten. Sdu Uitgevers bv.
• Dejaeger, Karel, Verbeke, Wouter, Martens, David, & Baesens, Bart. (2010). De kosten van software-ontwikkeling voorspellen (Vol. 52). Sdu Uitgevers bv.
• De Backer, Manu, & Baesens, Bart. (2009). BPMN 2.0: Meer dan een naamsverandering ?
• Verbeke, Wouter, & Baesens, Bart. (2009). Van credit crunch naar ICT crash, of niet ?Roularta.
• Baesens, Bart, De Backer, Manu, & Martens, David. (2009). Business intelligence process management = business process intelligence. Sdu Uitgevers bv.
• Dejaeger, Karel, Ruelens, Jessica, Van Gestel, Tony, Jacobs, Joachim, Baesens, Bart, Poelmans, Jonas, & Hamers, Bart. (2009). Evaluatie en verbetering van de datakwaliteit(Vol. 51). Sdu Uitgevers bv.
• Vuylsteke, .A, Poelmans, Jonas, & Baesens, Bart. (2009). Online zoekgedrag van consumenten: China vs West-Europa (Vol. 2009). Faculteit Economie en Bedrijfswetenschappen van de K.U.Leuven.
• Baesens, B. (2007). It’s the data, you stupid! (Vol. 25). Roularta.
• Haesen, Raf, Martens, David, De Backer, Manu, & Baesens, Bart. (2005). AntMiner , een systeem van kennis-ontginnende mieren. University Press Leuven; Leuven.
• Van Gestel, Tony, Baesens, Bart, & Vanthienen, Jan. (2004). De impact van Bazel II op IT (Vol. 46).
• Baesens, Bart, Mues, Christophe, & Vanthienen, Jan. (2003). Knowledge discovery in data: Naar performante én begrijpelijke modellen van bedrijfsintelligentie. University Press Leuven; Leuven.
• Baesens, Bart, Mues, Christophe, & Vanthienen, Jan. (2003). Knowledge discovery in data: Van academische denkoefening naar bedrijfsrelevante praktijk (Vol. 45).