Bart Baesens - researchr alias

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2011

Process discovery in event logs: An application in the telecom industryStijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens. asc, 11(2):1697-1710, 2011. [doi]

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive modelsJohan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, Bart Baesens. dss, 51(1):141-154, 2011. [doi]

Monitoring and backtesting churn modelsElen Lima, Christophe Mues, Bart Baesens. eswa, 38(1):975-982, 2011. [doi]

Editorial survey: swarm intelligence for data miningDavid Martens, Bart Baesens, Tom Fawcett. ml, 82(1):1-42, 2011. [doi]

Identifying financially successful start-up profiles with data miningDavid Martens, Christine Vanhoutte, Sophie De Winne, Bart Baesens, Luc Sels, Christophe Mues. eswa, 38(5):5794-5800, 2011. [doi]

Building comprehensible customer churn prediction models with advanced rule induction techniquesWouter Verbeke, David Martens, Christophe Mues, Bart Baesens. eswa, 38(3):2354-2364, 2011. [doi]

A robust F-measure for evaluating discovered process modelsJochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens. cidm 2011: 148-155 [doi]

2010

An overview and framework for PD backtesting and benchmarkingG. Castermans, David Martens, Tony Van Gestel, B. Hamers, Bart Baesens. jors, 61(3):359-373, 2010. [doi]

From linear to non-linear kernel based classifiers for bankruptcy predictionTony Van Gestel, Bart Baesens, David Martens. ijon, 73(16-18):2955-2970, 2010. [doi]

Credit rating prediction using Ant Colony OptimizationDavid Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen, Bart Baesens. jors, 61(4):561-573, 2010. [doi]

Software Effort Prediction Using Regression Rule Extraction from Neural NetworksRudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens. ictai 2010: 45-52 [doi]

A Critical Evaluation Study of Model-Log Metrics in Process DiscoveryJochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens. BPM 2011: 158-169 [doi]

2009

50 years of data mining and OR: upcoming trends and challengesBart Baesens, Christophe Mues, David Martens, Jan Vanthienen. jors, 60(S1), 2009. [doi]

Inferring comprehensible business/ICT alignment rulesBjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Stijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck. iam, 46(2):116-124, 2009. [doi]

Modeling churn using customer lifetime valueNicolas Glady, Bart Baesens, Christophe Croux. eor, 197(1):402-411, 2009. [doi]

A modified Pareto/NBD approach for predicting customer lifetime valueNicolas Glady, Bart Baesens, Christophe Croux. eswa, 36(2):2062-2071, 2009. [doi]

Robust Process Discovery with Artificial Negative EventsStijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens. jmlr, 10:1305-1340, 2009. [doi]

Domain knowledge integration in data mining using decision tables: case studies in churn predictionElen Lima, Christophe Mues, Bart Baesens. jors, 60(8):1096-1106, 2009. [doi]

Decompositional Rule Extraction from Support Vector Machines by Active LearningDavid Martens, Bart Baesens, Tony Van Gestel. tkde, 21(2):178-191, 2009. [doi]

A note on knowledge discovery using neural networks and its application to credit card screeningRudy Setiono, Bart Baesens, Christophe Mues. eor, 192(1):326-332, 2009. [doi]

Including Domain Knowledge in Customer Churn Prediction Using AntMiner+Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen. incdm 2009: 10-21

Fraud Detection in Statistics Education Based on the Compendium Platform and Reproducible ComputingPatrick Wessa, Bart Baesens. csie 2009: 50-54 [doi]

2008

Minerva: Sequential Covering for Rule ExtractionJohan Huysmans, Rudy Setiono, Bart Baesens, Jan Vanthienen. tsmc, 38(2):299-309, 2008. [doi]

A Data Miner s Approach to Country Corruption AnalysisJohan Huysmans, Bart Baesens, Jan Vanthienen. In Hsinchun Chen, Christopher C. Yang, editors, Intelligence and Security Informatics, Techniques and Applications. Volume 135 of Studies in Computational Intelligence, pages 227-247, Springer, 2008. [doi]

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel FindingsStefan Lessmann, Bart Baesens, Christophe Mues, Swantje Pietsch. TSE, 34(4):485-496, 2008. [doi]

Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit ScoringDavid Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens. In Joachim Diederich, editor, Rule Extraction from Support Vector Machines. Volume 80 of Studies in Computational Intelligence, pages 33-63, Springer, 2008. [doi]

Predicting going concern opinion with data miningDavid Martens, Liesbeth Bruynseels, Bart Baesens, Marleen Willekens, Jan Vanthienen. dss, 45(4):765-777, 2008. [doi]

Recursive Neural Network Rule Extraction for Data With Mixed AttributesRudy Setiono, Bart Baesens, Christophe Mues. tnn, 19(2):299-307, 2008. [doi]

Mining software repositories for comprehensible software fault prediction modelsOlivier Vandecruys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, Raf Haesen. jss, 81(5):823-839, 2008. [doi]

2007

Process Mining as First-Order Classification Learning on Logs with Negative EventsStijn Goedertier, David Martens, Bart Baesens, Raf Haesen, Jan Vanthienen. BPM 2008: 42-53 [doi]

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithmsF. Hoffmann, Bart Baesens, Christophe Mues, Tony Van Gestel, Jan Vanthienen. eor, 177(1):540-555, 2007. [doi]

A new approach for measuring rule set consistencyJohan Huysmans, Bart Baesens, Jan Vanthienen. DKE, 63(1):167-182, 2007. [doi]

Classification With Ant Colony OptimizationDavid Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens. tec, 11(5):651-665, 2007. [doi]

Comprehensible credit scoring models using rule extraction from support vector machinesDavid Martens, Bart Baesens, Tony Van Gestel, Jan Vanthienen. eor, 183(3):1466-1476, 2007. [doi]

2006

Special issue on intelligent information systems for financial engineeringBart Baesens, Christophe Mues, Tony Van Gestel, Jan Vanthienen. eswa, 30(3):413-414, 2006. [doi]

A process model to develop an internal rating system: Sovereign credit ratingsTony Van Gestel, Bart Baesens, Peter Van Dijcke, João Garcia, Johan A. K. Suykens, Jan Vanthienen. dss, 42(2):1131-1151, 2006. [doi]

Failure prediction with self organizing mapsJohan Huysmans, Bart Baesens, Jan Vanthienen, Tony Van Gestel. eswa, 30(3):479-487, 2006. [doi]

ITER: An Algorithm for Predictive Regression Rule ExtractionJohan Huysmans, Bart Baesens, Jan Vanthienen. dawak 2006: 270-279 [doi]

Country Corruption Analysis with Self Organizing Maps and Support Vector MachinesJohan Huysmans, David Martens, Bart Baesens, Jan Vanthienen, Tony Van Gestel. wisi 2006: 103-114 [doi]

Ants Constructing Rule-Based ClassifiersDavid Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet. In Ajith Abraham, Crina Grosan, Vitorino Ramos, editors, Swarm Intelligence in Data Mining. Volume 34 of Studies in Computational Intelligence, pages 21-43, Springer, 2006. [doi]

Ant-Based Approach to the Knowledge Fusion ProblemDavid Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues, Jan Vanthienen. antsw 2006: 84-95 [doi]

2005

A Stigmergy Based Approach to Data MiningManu De Backer, Raf Haesen, David Martens, Bart Baesens. ausai 2005: 975-978 [doi]

Confidence intervals for probabilistic network classifiersMichael Egmont-Petersen, A. J. Feelders, Bart Baesens. csda, 49(4):998-1019, 2005. [doi]

A Comprehensible SOM-Based Scoring SystemJohan Huysmans, Bart Baesens, Jan Vanthienen. mldm 2005: 80-89 [doi]

From Knowledge Discovery to Implementation: Developing Business Intelligence Systems using Decision TablesChristophe Mues, Bart Baesens, Jan Vanthienen. wm 2005: 439-443

From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision TablesChristophe Mues, Bart Baesens, Rudy Setiono, Jan Vanthienen. wm 2005: 483-495 [doi]

Filter- versus wrapper-based feature selection for credit scoringPetr Somol, Bart Baesens, Pavel Pudil, Jan Vanthienen. ijis, 20(10):985-999, 2005. [doi]

2004

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customersBart Baesens, Geert Verstraeten, Dirk Van den Poel, Michael Egmont-Petersen, Patrick Van Kenhove, Jan Vanthienen. eor, 156(2):508-523, 2004. [doi]

Benchmarking Least Squares Support Vector Machine ClassifiersTony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle. ml, 54(1):5-32, 2004. [doi]

Web Usage Mining with Time Constrained Association RulesJohan Huysmans, Christophe Mues, Jan Vanthienen, Bart Baesens. ICEIS 2004: 343-348

Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and DiagramsChristophe Mues, Johan Huysmans, Jan Vanthienen, Bart Baesens. ICEIS 2004: 226-232

Decision diagrams in machine learning: an empirical study on real-life credit-risk dataChristophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen. eswa, 27(2):257-264, 2004. [doi]

Decision Diagrams in Machine Learning: An Empirical Study on Real-Life Credit-Risk DataChristophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen. diagrams 2004: 395-397 [doi]

2003

Building Intelligent Credit Scoring Systems Using Decision TablesBart Baesens, Christophe Mues, Manu De Backer, Jan Vanthienen, Rudy Setiono. ICEIS 2003: 19-25

Using Neural Network Rule Extraction and Decision Tables for Credit - Risk EvaluationBart Baesens, Rudy Setiono, Christophe Mues, Jan Vanthienen. mansci, 49(3):312-329, 2003. [doi]

2002

2001

2000