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2020 – today
- 2024
[j43]Félix Vandervorst
, Wouter Verbeke, Tim Verdonck:
Claims fraud detection with uncertain labels. Adv. Data Anal. Classif. 18(1): 219-243 (2024)- [j42]Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
A new perspective on classification: Optimally allocating limited resources to uncertain tasks. Decis. Support Syst. 179: 114151 (2024) - [j41]Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Slowinski, Bart Baesens, Robert N. Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard Weber:
Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. 317(2): 249-272 (2024) - [j40]Manon Reusens, Alexander Stevens, Jonathan Tonglet, Johannes De Smedt, Wouter Verbeke, Seppe vanden Broucke, Bart Baesens:
Evaluating text classification: A benchmark study. Expert Syst. Appl. 254: 124302 (2024) - [j39]Simon De Vos, Johannes De Smedt, Marijke Verbruggen, Wouter Verbeke:
Data-driven internal mobility: Similarity regularization gets the job done. Knowl. Based Syst. 295: 111824 (2024) - [j38]Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor:
Target informed client recruitment for efficient federated learning in healthcare. BMC Medical Informatics Decis. Mak. 24(1): 380 (2024) - [j37]Jente Van Belle, Ruben Crevits, Daan Caljon, Wouter Verbeke:
Probabilistic Forecasting With Modified N-BEATS Networks. IEEE Trans. Neural Networks Learn. Syst. 35(12): 18872-18885 (2024) - [i27]Toon Vanderschueren, Wouter Verbeke, Felipe Moraes, Hugo Manuel Proença:
Metalearners for Ranking Treatment Effects. CoRR abs/2405.02183 (2024) - [i26]Bruno Deprez, Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
Network Analytics for Anti-Money Laundering - A Systematic Literature Review and Experimental Evaluation. CoRR abs/2405.19383 (2024) - [i25]Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke:
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation. CoRR abs/2406.08206 (2024) - [i24]Daan Caljon, Jeff Vercauteren, Simon De Vos, Wouter Verbeke, Jente Van Belle:
Using dynamic loss weighting to boost improvements in forecast stability. CoRR abs/2409.18267 (2024) - [i23]Daan Caljon, Jente Van Belle, Jeroen Berrevoets, Wouter Verbeke:
Optimizing Treatment Allocation in the Presence of Interference. CoRR abs/2410.00075 (2024) - [i22]Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann, Wouter Verbeke:
Uplift modeling with continuous treatments: A predict-then-optimize approach. CoRR abs/2412.09232 (2024) - 2023
- [j36]Simon De Vos, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke:
Robust instance-dependent cost-sensitive classification. Adv. Data Anal. Classif. 17(4): 1057-1079 (2023) - [j35]Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Vincent Ginis, Wouter Verbeke:
HydaLearn. Appl. Intell. 53(5): 5808-5822 (2023) - [j34]Wouter Verbeke, Diego Olaya, Marie-Anne Guerry, Jente Van Belle:
To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates. Eur. J. Oper. Res. 305(2): 838-852 (2023) - [j33]Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke:
Fraud analytics: A decade of research: Organizing challenges and solutions in the field. Expert Syst. Appl. 232: 120605 (2023) - [j32]Toon Vanderschueren, Jeroen Berrevoets, Wouter Verbeke:
NOFLITE: Learning to Predict Individual Treatment Effect Distributions. Trans. Mach. Learn. Res. 2023 (2023) - [c9]Hans Weytjens, Wouter Verbeke, Jochen De Weerdt:
Timed Process Interventions: Causal Inference vs. Reinforcement Learning. Business Process Management Workshops 2023: 245-258 - [c8]Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor:
Client Recruitment for Federated Learning in ICU Length of Stay Prediction. e-Science 2023: 1-9 - [c7]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML 2023: 34855-34874 - [e2]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Communications in Computer and Information Science 1752, Springer 2023, ISBN 978-3-031-23617-4 [contents] - [e1]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Communications in Computer and Information Science 1753, Springer 2023, ISBN 978-3-031-23632-7 [contents] - [i21]Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor:
Client Recruitment for Federated Learning in ICU Length of Stay Prediction. CoRR abs/2304.14663 (2023) - [i20]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR abs/2306.04255 (2023) - [i19]Hans Weytjens, Wouter Verbeke, Jochen De Weerdt:
Timing Process Interventions with Causal Inference and Reinforcement Learning. CoRR abs/2306.04299 (2023) - [i18]Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke:
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions. CoRR abs/2309.03730 (2023) - [i17]Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke:
Learning continuous-valued treatment effects through representation balancing. CoRR abs/2309.03731 (2023) - [i16]Théo Verhelst, Robin Petit, Wouter Verbeke, Gianluca Bontempi:
Uplift vs. predictive modeling: a theoretical analysis. CoRR abs/2309.12036 (2023) - 2022
- [j31]Jakob Raymaekers, Wouter Verbeke, Tim Verdonck:
Weight-of-evidence through shrinkage and spline binning for interpretable nonlinear classification. Appl. Soft Comput. 115: 108160 (2022) - [j30]George Petrides, Wouter Verbeke:
Cost-sensitive ensemble learning: a unifying framework. Data Min. Knowl. Discov. 36(1): 1-28 (2022) - [j29]Félix Vandervorst, Wouter Verbeke, Tim Verdonck:
Data misrepresentation detection for insurance underwriting fraud prevention. Decis. Support Syst. 159: 113798 (2022) - [j28]Sebastiaan Höppner, Bart Baesens, Wouter Verbeke, Tim Verdonck:
Instance-dependent cost-sensitive learning for detecting transfer fraud. Eur. J. Oper. Res. 297(1): 291-300 (2022) - [j27]Toon Vanderschueren, Tim Verdonck, Bart Baesens, Wouter Verbeke:
Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Inf. Sci. 594: 400-415 (2022) - [j26]Lize Coenen, Wouter Verbeke, Tias Guns:
Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods. J. Oper. Res. Soc. 73(1): 191-206 (2022) - [j25]George Petrides, Darie Moldovan, Lize Coenen, Tias Guns, Wouter Verbeke:
Cost-sensitive learning for profit-driven credit scoring. J. Oper. Res. Soc. 73(2): 338-350 (2022) - [j24]Floris Devriendt, Jente Van Belle, Tias Guns, Wouter Verbeke:
Learning to Rank for Uplift Modeling. IEEE Trans. Knowl. Data Eng. 34(10): 4888-4904 (2022) - [c6]Toon Vanderschueren, Wouter Verbeke, Bart Baesens, Tim Verdonck:
Instance-dependent cost-sensitive learning: do we really need it? HICSS 2022: 1-9 - [c5]Tim Verdonck, Wouter Verbeke, Maria Óskarsdóttir, Bart Baesens:
Introduction to the Minitrack on Fraud Detection Using Machine Learning. HICSS 2022: 1-2 - [i15]Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
A new perspective on classification: optimally allocating limited resources to uncertain tasks. CoRR abs/2202.04369 (2022) - [i14]Toon Vanderschueren, Robert N. Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke:
Prescriptive maintenance with causal machine learning. CoRR abs/2206.01562 (2022) - [i13]Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke:
Fraud Analytics: A Decade of Research - Organizing Challenges and Solutions in the Field. CoRR abs/2212.04329 (2022) - 2021
- [j23]Sebastián Maldonado, Jaime Miranda, Diego Olaya, Jonathan Vásquez, Wouter Verbeke:
Redefining profit metrics for boosting student retention in higher education. Decis. Support Syst. 143: 113493 (2021) - [j22]Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke:
Autoencoders for strategic decision support. Decis. Support Syst. 150: 113422 (2021) - [j21]Jente Van Belle, Tias Guns, Wouter Verbeke:
Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains. Eur. J. Oper. Res. 288(2): 466-479 (2021) - [j20]Floris Devriendt, Jeroen Berrevoets, Wouter Verbeke:
Why you should stop predicting customer churn and start using uplift models. Inf. Sci. 548: 497-515 (2021) - [i12]Diego Olaya, Wouter Verbeke, Jente Van Belle, Marie-Anne Guerry:
To do or not to do: cost-sensitive causal decision-making. CoRR abs/2101.01407 (2021) - [i11]Jakob Raymaekers, Wouter Verbeke, Tim Verdonck:
Weight-of-evidence 2.0 with shrinkage and spline-binning. CoRR abs/2101.01494 (2021) - 2020
- [j19]Diego Olaya, Kristof Coussement, Wouter Verbeke:
A survey and benchmarking study of multitreatment uplift modeling. Data Min. Knowl. Discov. 34(2): 273-308 (2020) - [j18]Diego Olaya, Jonathan Vásquez, Sebastián Maldonado, Jaime Miranda, Wouter Verbeke:
Uplift Modeling for preventing student dropout in higher education. Decis. Support Syst. 134: 113320 (2020) - [j17]Cedric De Cauwer, Wouter Verbeke, Joeri Van Mierlo, Thierry Coosemans:
A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions. IEEE Trans. Intell. Transp. Syst. 21(7): 2787-2800 (2020) - [i10]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry. CoRR abs/2001.06700 (2020) - [i9]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture. CoRR abs/2001.06701 (2020) - [i8]Floris Devriendt, Tias Guns, Wouter Verbeke:
Learning to rank for uplift modeling. CoRR abs/2002.05897 (2020) - [i7]Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke:
Autoencoders for strategic decision support. CoRR abs/2005.01075 (2020) - [i6]Leonidas Siozos-Rousoulis, Dimitri Robert, Wouter Verbeke:
A study of the U.S. domestic air transportation network: Temporal evolution of network topology and robustness from 2001 to 2016. CoRR abs/2005.01101 (2020) - [i5]George Petrides, Wouter Verbeke:
Misclassification cost-sensitive ensemble learning: A unifying framework. CoRR abs/2007.07361 (2020) - [i4]Wouter Verbeke, Diego Olaya, Jeroen Berrevoets, Sebastián Maldonado:
The foundations of cost-sensitive causal classification. CoRR abs/2007.12582 (2020) - [i3]Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Wouter Verbeke:
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks. CoRR abs/2008.11643 (2020)
2010 – 2019
- 2019
- [j16]Steven Debaere, Floris Devriendt, Johanna Brunneder, Wouter Verbeke, Tom De Ruyck, Kristof Coussement:
Reducing inferior member community participation using uplift modeling: Evidence from a field experiment. Decis. Support Syst. 123 (2019) - [j15]Sheida Hadavi, Sara Verlinde, Wouter Verbeke, Cathy Macharis, Tias Guns:
Monitoring Urban-Freight Transport Based on GPS Trajectories of Heavy-Goods Vehicles. IEEE Trans. Intell. Transp. Syst. 20(10): 3747-3758 (2019) - [i2]Jeroen Berrevoets, Wouter Verbeke:
Causal Simulations for Uplift Modeling. CoRR abs/1902.00287 (2019) - [i1]Jeroen Berrevoets, Sam Verboven, Wouter Verbeke:
Optimising Individual-Treatment-Effect Using Bandits. CoRR abs/1910.07265 (2019) - 2018
- [j14]Bart Baesens, Wouter Verbeke, Cristián Bravo:
Special Issue on Profit-Driven Analytics. Big Data 6(1): 1-2 (2018) - [j13]Floris Devriendt, Darie Moldovan, Wouter Verbeke:
A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics. Big Data 6(1): 13-41 (2018) - [j12]Franco Garrido, Wouter Verbeke, Cristián Bravo:
A Robust profit measure for binary classification model evaluation. Expert Syst. Appl. 92: 154-160 (2018) - 2017
- [j11]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) - [j10]Frederik Gailly, Nadejda Alkhaldi, Sven Casteleyn, Wouter Verbeke:
Recommendation-Based Conceptual Modeling and Ontology Evolution Framework (CMOE+). Bus. Inf. Syst. Eng. 59(4): 235-250 (2017) - [j9]María Óskarsdóttir, 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) - 2016
- [c4]María Óskarsdóttir, 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 - 2014
- [j8]Wouter Verbeke, David Martens, Bart Baesens:
Social network analysis for customer churn prediction. Appl. Soft Comput. 14: 431-446 (2014) - [j7]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Predicting online channel acceptance with social network data. Decis. Support Syst. 63: 104-114 (2014) - [j6]Thomas Verbraken, Wouter Verbeke, Bart Baesens:
Profit optimizing customer churn prediction with Bayesian network classifiers. Intell. Data Anal. 18(1): 3-24 (2014) - 2013
- [j5]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) - 2012
- [b1]Wouter Verbeke:
Profit driven data mining in massive customer networks: new insights and algorithms. Katholieke Universiteit Leuven, Belgium, 2012 - [j4]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. Eur. J. Oper. Res. 218(1): 211-229 (2012) - [j3]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) - 2011
- [j2]David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens:
Performance of classification models from a user perspective. Decis. Support Syst. 51(4): 782-793 (2011) - [j1]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) - [c3]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Using Social Network Classifiers for Predicting E-Commerce Adoption. WEB 2011: 9-21 - 2010
- [c2]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
2000 – 2009
- 2009
- [c1]Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen:
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. DMM@ICDM 2009: 10-21
Coauthor Index
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