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2010 – today
- 2019
[j103]Maria Oskarsdottir, Cristián Bravo, Carlos Sarraute, Jan Vanthienen, Bart Baesens:
The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Appl. Soft Comput. 74: 26-39 (2019)- [i2]Libo Li, Stefan Lessmann, Bart Baesens:
Evaluating software defect prediction performance: an updated benchmarking study. CoRR abs/1901.01726 (2019) - 2018
- [j102]Jasmien Lismont, Eddy Cardinaels, Liesbeth Bruynseels, Sander De Groote, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Predicting tax avoidance by means of social network analytics. Decision Support Systems 108: 13-24 (2018) - [j101]Eugen Stripling, Bart Baesens, Barak Chizi, Seppe vanden Broucke:
Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers' compensation fraud. Decision Support Systems 111: 13-26 (2018) - [j100]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
On the operational efficiency of different feature types for telco Churn prediction. European Journal of Operational Research 267(3): 1141-1155 (2018) - [j99]Jasmien Lismont, Sudha Ram, Jan Vanthienen, Wilfried Lemahieu, Bart Baesens:
Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation. Expert Syst. Appl. 104: 22-32 (2018) - [j98]Maria Oskarsdottir, Tine Van Calster, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Time series for early churn detection: Using similarity based classification for dynamic networks. Expert Syst. Appl. 106: 55-65 (2018) - [j97]Bing Zhu, Bart Baesens, Aimée Backiel, Seppe K. L. M. vanden Broucke:
Benchmarking sampling techniques for imbalance learning in churn prediction. JORS 69(1): 49-65 (2018) - [j96]Michael Reusens, Wilfried Lemahieu, Bart Baesens, Luc Sels:
Evaluating recommendation and search in the labor market. Knowl.-Based Syst. 152: 62-69 (2018) - [j95]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation 40: 116-130 (2018) - [j94]Klaas Nelissen, Monique Snoeck, Seppe K. L. M. vanden Broucke, Bart Baesens:
Swipe and Tell: Using Implicit Feedback to Predict User Engagement on Tablets. ACM Trans. Inf. Syst. 36(4): 35:1-35:36 (2018) - [c58]Sam De Winter, Tim Decuypere, Sandra Mitrovic, Bart Baesens, Jochen De Weerdt:
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction. ASONAM 2018: 1234-1241 - [c57]Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme:
Profit Maximizing Logistic Regression Modeling for Credit Scoring. DSW 2018: 125-129 - [c56]Maria Oskarsdottir, Cristián Bravo, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending. ICIS 2018 - 2017
- [j93]Tine Van Calster, Bart Baesens, Wilfried Lemahieu:
ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Appl. Soft Comput. 60: 775-785 (2017) - [j92]Wouter Verbeke, David Martens, Bart Baesens:
RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Appl. Soft Comput. 60: 858-873 (2017) - [j91]Libo Li, Frank Goethals, Bart Baesens, Monique Snoeck:
Predicting software revision outcomes on GitHub using structural holes theory. Computer Networks 114: 114-124 (2017) - [j90]Michael Reusens, Wilfried Lemahieu, Bart Baesens, Luc Sels:
A note on explicit versus implicit information for job recommendation. Decision Support Systems 98: 26-35 (2017) - [j89]Jan Mendling, Bart Baesens, Abraham Bernstein, Michael Fellmann:
Challenges of smart business process management: An introduction to the special issue. Decision Support Systems 100: 1-5 (2017) - [j88]Maria Oskarsdottir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Social network analytics for churn prediction in telco: Model building, evaluation and network architecture. Expert Syst. Appl. 85: 204-220 (2017) - [j87]Jasmien Lismont, Jan Vanthienen, Bart Baesens, Wilfried Lemahieu:
Defining analytics maturity indicators: A survey approach. Int J. Information Management 37(3): 114-124 (2017) - [j86]Bing Zhu, Bart Baesens, Seppe K. L. M. vanden Broucke:
An empirical comparison of techniques for the class imbalance problem in churn prediction. Inf. Sci. 408: 84-99 (2017) - [j85]Lore Dirick, Gerda Claeskens, Bart Baesens:
Time to default in credit scoring using survival analysis: a benchmark study. JORS 68(6): 652-665 (2017) - [j84]Véronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
GOTCHA! Network-Based Fraud Detection for Social Security Fraud. Management Science 63(9): 3090-3110 (2017) - [j83]Bing Zhu, Yongge Niu, Jin Xiao, Bart Baesens:
A new transferred feature selection algorithm for customer identification. Neural Computing and Applications 28(9): 2593-2603 (2017) - [c55]Pieter De Koninck, Klaas Nelissen, Bart Baesens, Seppe vanden Broucke, Monique Snoeck, Jochen De Weerdt:
An Approach for Incorporating Expert Knowledge in Trace Clustering. CAiSE 2017: 561-576 - [c54]Sandra Mitrovic, Gaurav Singh, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Scalable RFM-enriched Representation Learning for Churn Prediction. DSAA 2017: 79-88 - [c53]Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado:
Improving Resampling-based Ensemble in Churn Prediction. LIDTA@PKDD/ECML 2017: 79-91 - [c52]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Churn Prediction Using Dynamic RFM-Augmented Node2vec. PAP@PKDD/ECML 2017: 122-138 - [i1]Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck:
Profit Driven Decision Trees for Churn Prediction. CoRR abs/1712.08101 (2017) - 2016
- [j82]Xinwei Zhu, Seppe vanden Broucke, Guobin Zhu, Jan Vanthienen, Bart Baesens:
Enabling flexible location-aware business process modeling and execution. Decision Support Systems 83: 1-9 (2016) - [j81]Helen-Tadesse Moges, Véronique Van Vlasselaer, Wilfried Lemahieu, Bart Baesens:
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 (2016) - [j80]Seppe K. L. M. vanden Broucke, Filip Caron, Jasmien Lismont, Jan Vanthienen, Bart Baesens:
On the gap between reality and registration: a business event analysis classification framework. Information Technology and Management 17(4): 393-410 (2016) - [j79]Aimée Backiel, Bart Baesens, Gerda Claeskens:
Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. JORS 67(9) (2016) - [c51]Maria Oskarsdottir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
A comparative study of social network classifiers for predicting churn in the telecommunication industry. ASONAM 2016: 1151-1158 - 2015
- [j78]Sebastián Maldonado, Álvaro Flores, Thomas Verbraken, Bart Baesens, Richard Weber:
Profit-based feature selection using support vector machines - General framework and an application for customer retention. Appl. Soft Comput. 35: 740-748 (2015) - [j77]Bart Minnaert, David Martens, Manu De Backer, Bart Baesens:
To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms. Data Min. Knowl. Discov. 29(1): 237-272 (2015) - [j76]Véronique Van Vlasselaer, Cristián Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems 75: 38-48 (2015) - [j75]Lore Dirick, Gerda Claeskens, Bart Baesens:
An Akaike information criterion for multiple event mixture cure models. European Journal of Operational Research 241(2): 449-457 (2015) - [j74]Stefan Lessmann, Bart Baesens, Hsin-Vonn Seow, Lyn C. Thomas:
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research 247(1): 124-136 (2015) - [j73]Alex Seret, Sebastián Maldonado, Bart Baesens:
Identifying next relevant variables for segmentation by using feature selection approaches. Expert Syst. Appl. 42(15-16): 6255-6266 (2015) - [j72]Alex Seret, Andreea Bejinaru, Bart Baesens:
Domain knowledge based segmentation of online banking customers. Intell. Data Anal. 19(s1): S163-S184 (2015) - [j71]Julie Moeyersoms, Enric Junqué de Fortuny, Karel Dejaeger, Bart Baesens, David Martens:
Comprehensible software fault and effort prediction: A data mining approach. Journal of Systems and Software 100: 80-90 (2015) - [c50]Aimée Backiel, Yannick Verbinnen, Bart Baesens, Gerda Claeskens:
Combining Local and Social Network Classifiers to Improve Churn Prediction. ASONAM 2015: 651-658 - [c49]Véronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
AFRAID: Fraud Detection via Active Inference in Time-evolving Social Networks. ASONAM 2015: 659-666 - [c48]Carlos André R. Pinheiro, Véronique Van Vlasselaer, Bart Baesens, Alexandre G. Evsukoff, Moacyr A. H. B. Silva, Nelson F. F. Ebecken:
A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data. CSOC (2) 2015: 35-44 - [c47]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic regression modeling for customer churn prediction. DSAA 2015: 1-10 - [c46]Véronique Van Vlasselaer, Leman Akoglu, Tina Eliassi-Rad, Monique Snoeck, Bart Baesens:
Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships. HICSS 2015: 918-927 - 2014
- [j70]Wouter Verbeke, David Martens, Bart Baesens:
Social network analysis for customer churn prediction. Appl. Soft Comput. 14: 431-446 (2014) - [j69]Alex Seret, Thomas Verbraken, Bart Baesens:
A new knowledge-based constrained clustering approach: Theory and application in direct marketing. Appl. Soft Comput. 24: 316-327 (2014) - [j68]Filip Caron, Jan Vanthienen, Kris Vanhaecht, Erik van Limbergen, Jochen De Weerdt, Bart Baesens:
Monitoring care processes in the gynecologic oncology department. Comp. in Bio. and Med. 44: 88-96 (2014) - [j67]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Predicting online channel acceptance with social network data. Decision Support Systems 63: 104-114 (2014) - [j66]Thomas Verbraken, Cristián Bravo, Richard Weber, Bart Baesens:
Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research 238(2): 505-513 (2014) - [j65]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Syst. Appl. 41(10): 4648-4657 (2014) - [j64]Thomas Verbraken, Wouter Verbeke, Bart Baesens:
Profit optimizing customer churn prediction with Bayesian network classifiers. Intell. Data Anal. 18(1): 3-24 (2014) - [j63]Baojun Ma, Huaping Zhang, Guoqing Chen, Yanping Zhao, Bart Baesens:
Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective. International Journal of Software Engineering and Knowledge Engineering 24(1): 61-90 (2014) - [j62]
- [j61]Ellen Tobback, David Martens, Tony Van Gestel, Bart Baesens:
Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state. JORS 65(3): 376-392 (2014) - [j60]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Trans. Knowl. Data Eng. 26(8): 1877-1889 (2014) - [c45]Xinwei Zhu, Guobin Zhu, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Towards Location-Aware Process Modeling and Execution. Business Process Management Workshops 2014: 186-197 - [c44]Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Declarative process discovery with evolutionary computing. IEEE Congress on Evolutionary Computation 2014: 2412-2419 - [c43]Aimée Backiel, Bart Baesens, Gerda Claeskens:
Mining Telecommunication Networks to Enhance Customer Lifetime Predictions. ICAISC (2) 2014: 15-26 - [c42]Seppe K. L. M. vanden Broucke, Jorge Munoz-Gama, Josep Carmona, Bart Baesens, Jan Vanthienen:
Event-Based Real-Time Decomposed Conformance Analysis. OTM Conferences 2014: 345-363 - 2013
- [j59]Jochen De Weerdt, Annelies Schupp, An Vanderloock, Bart Baesens:
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 (2013) - [j58]Filip Caron, Jan Vanthienen, Bart Baesens:
A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Computers in Industry 64(4): 464-475 (2013) - [j57]Filip Caron, Jan Vanthienen, Bart Baesens:
Comprehensive rule-based compliance checking and risk management with process mining. Decision Support Systems 54(3): 1357-1369 (2013) - [j56]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
A multidimensional analysis of data quality for credit risk management: New insights and challenges. Information & Management 50(1): 43-58 (2013) - [j55]Thomas Verbraken, Wouter Verbeke, Bart Baesens:
A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models. IEEE Trans. Knowl. Data Eng. 25(5): 961-973 (2013) - [j54]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Active Trace Clustering for Improved Process Discovery. IEEE Trans. Knowl. Data Eng. 25(12): 2708-2720 (2013) - [j53]Karel Dejaeger, Thomas Verbraken, Bart Baesens:
Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers. IEEE Trans. Software Eng. 39(2): 237-257 (2013) - [c41]Véronique Van Vlasselaer, Jan Meskens, Dries Van Dromme, Bart Baesens:
Using social network knowledge for detecting spider constructions in social security fraud. ASONAM 2013: 813-820 - [c40]Seppe K. L. M. vanden Broucke, Cédric Delvaux, João Freitas, Taisiia Rogova, Jan Vanthienen, Bart Baesens:
Uncovering the Relationship Between Event Log Characteristics and Process Discovery Techniques. Business Process Management Workshops 2013: 41-53 - [c39]Seppe K. L. M. vanden Broucke, Filip Caron, Jan Vanthienen, Bart Baesens:
Validating and Enhancing Declarative Business Process Models Based on Allowed and Non-occurring Past Behavior. Business Process Management Workshops 2013: 212-223 - [c38]
- [c37]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
An Exploratory Approach for Understanding Customer Behavior Processes Based on Clustering and Sequence Mining. Business Process Management Workshops 2013: 237-248 - [c36]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. CIDM 2013: 254-261 - [c35]Filip Caron, Jan Vanthienen, Bart Baesens:
Business Rule Patterns and Their Application to Process Analytics. EDOC Workshops 2013: 13-20 - [c34]Libo Li, Frank Goethals, Antonio Giangreco, Bart Baesens:
Using social network data to predict technology acceptance. ICIS 2013 - 2012
- [j52]Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, Bart Baesens:
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research 218(1): 211-229 (2012) - [j51]Karel Dejaeger, Frank Goethals, Antonio Giangreco, Lapo Mola, Bart Baesens:
Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research 218(2): 548-562 (2012) - [j50]Alex Seret, Thomas Verbraken, Sébastien Versailles, Bart Baesens:
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) - [j49]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
A total data quality management for credit risk: new insights and challenges. IJIQ 3(1): 1-27 (2012) - [j48]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7): 654-676 (2012) - [j47]Bart Baesens, Pantelis Bouboulis, Sergio Cruces, Carlotta Domeniconi, Shiro Ikeda, Xuelong Li, Patricia Melin, Vadrevu Sree Hari Rao, Björn W. Schuller, Yi Shen, Huajin Tang, Cong Wang, Jian Yang, Derong Zhao, D. Liu:
Neural Networks and Learning Systems Come Together. IEEE Trans. Neural Netw. Learning Syst. 23(1): 1-6 (2012) - [j46]Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens:
Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans. Software Eng. 38(2): 375-397 (2012) - [c33]Filip Caron, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
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. AMCIS 2012 - [c32]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Bart Baesens, Jan Vanthienen:
Improved Artificial Negative Event Generation to Enhance Process Event Logs. CAiSE 2012: 254-269 - [c31]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c30]Jochen De Weerdt, Filip Caron, Jan Vanthienen, Bart Baesens:
Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining. PAKDD Workshops 2012: 22-35 - 2011
- [j45]Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens:
Process discovery in event logs: An application in the telecom industry. Appl. Soft Comput. 11(2): 1697-1710 (2011) - [j44]Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, Bart Baesens:
An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems 51(1): 141-154 (2011) - [j43]David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens:
Performance of classification models from a user perspective. Decision Support Systems 51(4): 782-793 (2011) - [j42]Elen Lima, Christophe Mues, Bart Baesens:
Monitoring and backtesting churn models. Expert Syst. Appl. 38(1): 975-982 (2011) - [j41]Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens:
Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38(3): 2354-2364 (2011) - [j40]David Martens, Christine Vanhoutte, Sophie De Winne, Bart Baesens, Luc Sels, Christophe Mues:
Identifying financially successful start-up profiles with data mining. Expert Syst. Appl. 38(5): 5794-5800 (2011) - [j39]Rudy Setiono, Bart Baesens, Christophe Mues:
Rule Extraction from Minimal Neural Networks for Credit Card Screening. Int. J. Neural Syst. 21(4): 265-276 (2011) - [j38]David Martens, Bart Baesens, Tom Fawcett:
Editorial survey: swarm intelligence for data mining. Machine Learning 82(1): 1-42 (2011) - [j37]Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada:
Guest Editorial White Box Nonlinear Prediction Models. IEEE Trans. Neural Networks 22(12): 2406-2408 (2011) - [c29]Filip Caron, Jan Vanthienen, Jochen De Weerdt, Bart Baesens:
Advanced Care-Flow Mining and Analysis. Business Process Management Workshops (1) 2011: 167-168 - [c28]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A robust F-measure for evaluating discovered process models. CIDM 2011: 148-155 - [c27]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
Data quality for credit risk management. ICIQ 2011 - [c26]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Using Social Network Classifiers for Predicting E-Commerce Adoption. WEB 2011: 9-21 - 2010
- [j36]Tony Van Gestel, Bart Baesens, David Martens:
From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73(16-18): 2955-2970 (2010) - [j35]G. Castermans, David Martens, Tony Van Gestel, Bart Hamers, Bart Baesens:
An overview and framework for PD backtesting and benchmarking. JORS 61(3): 359-373 (2010) - [j34]David Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen, Bart Baesens:
Credit rating prediction using Ant Colony Optimization. JORS 61(4): 561-573 (2010) - [c25]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A Critical Evaluation Study of Model-Log Metrics in Process Discovery. Business Process Management Workshops 2010: 158-169 - [c24]Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens:
Software Effort Prediction Using Regression Rule Extraction from Neural Networks. ICTAI (2) 2010: 45-52 - [c23]Karel Dejaeger, Bart Hamers, Jonas Poelmans, Bart Baesens:
A novel approach to the evaluation and improvement of data quality in the financial sector. ICIQ 2010 - [p4]
2000 – 2009
- 2009
- [j33]Rudy Setiono, Bart Baesens, Christophe Mues:
A note on knowledge discovery using neural networks and its application to credit card screening. European Journal of Operational Research 192(1): 326-332 (2009) - [j32]Nicolas Glady, Bart Baesens, Christophe Croux:
Modeling churn using customer lifetime value. European Journal of Operational Research 197(1): 402-411 (2009) - [j31]Nicolas Glady, Bart Baesens, Christophe Croux:
A modified Pareto/NBD approach for predicting customer lifetime value. Expert Syst. Appl. 36(2): 2062-2071 (2009) - [j30]Bjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Stijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck:
Inferring comprehensible business/ICT alignment rules. Information & Management 46(2): 116-124 (2009) - [j29]Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens:
Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10: 1305-1340 (2009) - [j28]Elen Lima, Christophe Mues, Bart Baesens:
Domain knowledge integration in data mining using decision tables: case studies in churn prediction. JORS 60(8): 1096-1106 (2009) - [j27]Bart Baesens, Christophe Mues, David Martens, Jan Vanthienen:
50 years of data mining and OR: upcoming trends and challenges. JORS 60(S1) (2009) - [j26]David Martens, Bart Baesens, Tony Van Gestel:
Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Trans. Knowl. Data Eng. 21(2): 178-191 (2009) - [c22]Patrick Wessa, Bart Baesens:
Fraud Detection in Statistics Education Based on the Compendium Platform and Reproducible Computing. CSIE (3) 2009: 50-54 - [c21]Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen:
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. Industrial Conference on Data Mining - Workshop DMM 2009: 10-21 - 2008
- [j25]