Wouter Verbeke

According to our database, Wouter Verbeke authored at least 70 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Claims fraud detection with uncertain labels.

Félix Vandervorst, Wouter Verbeke, Tim Verdonck

Adv. Data Anal. Classif., March, 2024

Data-driven internal mobility: Similarity regularization gets the job done.

Simon De Vos, Johannes De Smedt, Marijke Verbruggen, Wouter Verbeke

Knowl. Based Syst., 2024

Evaluating text classification: A benchmark study.

Manon Reusens, Alexander Stevens, Jonathan Tonglet, Johannes De Smedt, Wouter Verbeke, Seppe vanden Broucke, Bart Baesens

Expert Syst. Appl., 2024

Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda.

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

Eur. J. Oper. Res., 2024

A new perspective on classification: Optimally allocating limited resources to uncertain tasks.

Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke

Decis. Support Syst., 2024

Optimizing Treatment Allocation in the Presence of Interference.

Daan Caljon, Jente Van Belle, Jeroen Berrevoets, Wouter Verbeke

CoRR, 2024

Using dynamic loss weighting to boost improvements in forecast stability.

Daan Caljon, Jeff Vercauteren, Simon De Vos, Wouter Verbeke, Jente Van Belle

CoRR, 2024

Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation.

Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke

CoRR, 2024

Network Analytics for Anti-Money Laundering - A Systematic Literature Review and Experimental Evaluation.

Bruno Deprez, Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke

CoRR, 2024

Metalearners for Ranking Treatment Effects.

Toon Vanderschueren, Wouter Verbeke, Felipe Moraes, Hugo Manuel Proença

CoRR, 2024

2023

Fraud analytics: A decade of research: Organizing challenges and solutions in the field.

Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke

Expert Syst. Appl., December, 2023

Robust instance-dependent cost-sensitive classification.

Simon De Vos, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke

Adv. Data Anal. Classif., December, 2023

HydaLearn.

Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Vincent Ginis, Wouter Verbeke

Appl. Intell., March, 2023

NOFLITE: Learning to Predict Individual Treatment Effect Distributions.

Toon Vanderschueren, Jeroen Berrevoets, Wouter Verbeke

Trans. Mach. Learn. Res., 2023

To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates.

Wouter Verbeke, Diego Olaya, Marie-Anne Guerry, Jente Van Belle

Eur. J. Oper. Res., 2023

Uplift vs. predictive modeling: a theoretical analysis.

Théo Verhelst, Robin Petit, Wouter Verbeke, Gianluca Bontempi

CoRR, 2023

Learning continuous-valued treatment effects through representation balancing.

Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke

CoRR, 2023

A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions.

Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke

CoRR, 2023

Timing Process Interventions with Causal Inference and Reinforcement Learning.

Hans Weytjens, Wouter Verbeke, Jochen De Weerdt

CoRR, 2023

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time.

Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

Client Recruitment for Federated Learning in ICU Length of Stay Prediction.

Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor

Proceedings of the 19th IEEE International Conference on e-Science, 2023

Timed Process Interventions: Causal Inference vs. Reinforcement Learning.

Hans Weytjens, Wouter Verbeke, Jochen De Weerdt

Proceedings of the Business Process Management Workshops, 2023

2022

Learning to Rank for Uplift Modeling.

Floris Devriendt, Jente Van Belle, Tias Guns, Wouter Verbeke

IEEE Trans. Knowl. Data Eng., 2022

Cost-sensitive learning for profit-driven credit scoring.

George Petrides, Darie Moldovan, Lize Coenen, Tias Guns, Wouter Verbeke

J. Oper. Res. Soc., 2022

Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods.

Lize Coenen, Wouter Verbeke, Tias Guns

J. Oper. Res. Soc., 2022

Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies.

Toon Vanderschueren, Tim Verdonck, Bart Baesens, Wouter Verbeke

Inf. Sci., 2022

Instance-dependent cost-sensitive learning for detecting transfer fraud.

Sebastiaan Höppner, Bart Baesens, Wouter Verbeke, Tim Verdonck

Eur. J. Oper. Res., 2022

Data misrepresentation detection for insurance underwriting fraud prevention.

Félix Vandervorst, Wouter Verbeke, Tim Verdonck

Decis. Support Syst., 2022

Cost-sensitive ensemble learning: a unifying framework.

George Petrides, Wouter Verbeke

Data Min. Knowl. Discov., 2022

Prescriptive maintenance with causal machine learning.

Toon Vanderschueren, Robert N. Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke

CoRR, 2022

Weight-of-evidence through shrinkage and spline binning for interpretable nonlinear classification.

Jakob Raymaekers, Wouter Verbeke, Tim Verdonck

Appl. Soft Comput., 2022

Introduction to the Minitrack on Fraud Detection Using Machine Learning.

Tim Verdonck, Wouter Verbeke, Maria Óskarsdóttir, Bart Baesens

Proceedings of the 55th Hawaii International Conference on System Sciences, 2022

Instance-dependent cost-sensitive learning: do we really need it?

Toon Vanderschueren, Wouter Verbeke, Bart Baesens, Tim Verdonck

Proceedings of the 55th Hawaii International Conference on System Sciences, 2022

2021

Why you should stop predicting customer churn and start using uplift models.

Floris Devriendt, Jeroen Berrevoets, Wouter Verbeke

Inf. Sci., 2021

Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains.

Jente Van Belle, Tias Guns, Wouter Verbeke

Eur. J. Oper. Res., 2021

Autoencoders for strategic decision support.

Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke

Decis. Support Syst., 2021

Redefining profit metrics for boosting student retention in higher education.

Sebastián Maldonado, Jaime Miranda, Diego Olaya, Jonathan Vásquez, Wouter Verbeke

Decis. Support Syst., 2021

Weight-of-evidence 2.0 with shrinkage and spline-binning.

Jakob Raymaekers, Wouter Verbeke, Tim Verdonck

CoRR, 2021

To do or not to do: cost-sensitive causal decision-making.

Diego Olaya, Wouter Verbeke, Jente Van Belle, Marie-Anne Guerry

CoRR, 2021

2020

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions.

Cedric De Cauwer, Wouter Verbeke, Joeri Van Mierlo, Thierry Coosemans

IEEE Trans. Intell. Transp. Syst., 2020

Uplift Modeling for preventing student dropout in higher education.

Diego Olaya, Jonathan Vásquez, Sebastián Maldonado, Jaime Miranda, Wouter Verbeke

Decis. Support Syst., 2020

A survey and benchmarking study of multitreatment uplift modeling.

Diego Olaya, Kristof Coussement, Wouter Verbeke

Data Min. Knowl. Discov., 2020

HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks.

Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Wouter Verbeke

CoRR, 2020

The foundations of cost-sensitive causal classification.

Wouter Verbeke, Diego Olaya, Jeroen Berrevoets, Sebastián Maldonado

CoRR, 2020

Misclassification cost-sensitive ensemble learning: A unifying framework.

George Petrides, Wouter Verbeke

CoRR, 2020

A study of the U.S. domestic air transportation network: Temporal evolution of network topology and robustness from 2001 to 2016.

Leonidas Siozos-Rousoulis, Dimitri Robert, Wouter Verbeke

CoRR, 2020

Learning to rank for uplift modeling.

Floris Devriendt, Tias Guns, Wouter Verbeke

CoRR, 2020

2019

Monitoring Urban-Freight Transport Based on GPS Trajectories of Heavy-Goods Vehicles.

Sheida Hadavi, Sara Verlinde, Wouter Verbeke, Cathy Macharis, Tias Guns

IEEE Trans. Intell. Transp. Syst., 2019

Reducing inferior member community participation using uplift modeling: Evidence from a field experiment.

Steven Debaere, Floris Devriendt, Johanna Brunneder, Wouter Verbeke, Tom De Ruyck, Kristof Coussement

Decis. Support Syst., 2019

Optimising Individual-Treatment-Effect Using Bandits.

Jeroen Berrevoets, Sam Verboven, Wouter Verbeke

CoRR, 2019

Causal Simulations for Uplift Modeling.

Jeroen Berrevoets, Wouter Verbeke

CoRR, 2019

2018

A Robust profit measure for binary classification model evaluation.

Franco Garrido, Wouter Verbeke, Cristián Bravo

Expert Syst. Appl., 2018

A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics.

Floris Devriendt, Darie Moldovan, Wouter Verbeke

Big Data, 2018

Special Issue on Profit-Driven Analytics.

Bart Baesens, Wouter Verbeke, Cristián Bravo

Big Data, 2018

2017

Social network analytics for churn prediction in telco: Model building, evaluation and network architecture.

María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen

Expert Syst. Appl., 2017

Recommendation-Based Conceptual Modeling and Ontology Evolution Framework (CMOE+).

Frederik Gailly, Nadejda Alkhaldi, Sven Casteleyn, Wouter Verbeke

Bus. Inf. Syst. Eng., 2017

RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints.

Wouter Verbeke, David Martens, Bart Baesens

Appl. Soft Comput., 2017

2016

A comparative study of social network classifiers for predicting churn in the telecommunication industry.

María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2016

2014

Profit optimizing customer churn prediction with Bayesian network classifiers.

Thomas Verbraken, Wouter Verbeke, Bart Baesens

Intell. Data Anal., 2014

Predicting online channel acceptance with social network data.

Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens

Decis. Support Syst., 2014

Social network analysis for customer churn prediction.

Wouter Verbeke, David Martens, Bart Baesens

Appl. Soft Comput., 2014

2013

A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models.

Thomas Verbraken, Wouter Verbeke, Bart Baesens

IEEE Trans. Knowl. Data Eng., 2013

2012

Profit driven data mining in massive customer networks: new insights and algorithms.

Wouter Verbeke

PhD thesis, 2012

Data Mining Techniques for Software Effort Estimation: A Comparative Study.

Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens

IEEE Trans. Software Eng., 2012

New insights into churn prediction in the telecommunication sector: A profit driven data mining approach.

Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, Bart Baesens

Eur. J. Oper. Res., 2012

2011

Building comprehensible customer churn prediction models with advanced rule induction techniques.

Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens

Expert Syst. Appl., 2011

Performance of classification models from a user perspective.

David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens

Decis. Support Syst., 2011

Using Social Network Classifiers for Predicting E-Commerce Adoption.

Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens

Proceedings of the E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, 2011

2010

Software Effort Prediction Using Regression Rule Extraction from Neural Networks.

Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens

Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010

2009

Including Domain Knowledge in Customer Churn Prediction Using AntMiner+.

Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen

Proceedings of the Advances in Data Mining in Marketing. 9th Industrial Conference, 2009