Wouter Verbeke
Wouter Verbeke
Professor of Decision Science, KU Leuven, Faculty of Economics and Business
Verified email at - Homepage
Cited by
Cited by
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach
W Verbeke, K Dejaeger, D Martens, J Hur, B Baesens
European journal of operational research 218 (1), 211-229, 2012
Building comprehensible customer churn prediction models with advanced rule induction techniques
W Verbeke, D Martens, C Mues, B Baesens
Expert systems with applications 38 (3), 2354-2364, 2011
Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection
B Baesens, V Van Vlasselaer, W Verbeke
John Wiley & Sons, 2015
Data mining techniques for software effort estimation: a comparative study
K Dejaeger, W Verbeke, D Martens, B Baesens
IEEE transactions on software engineering 38 (2), 375-397, 2011
Online state of health estimation on NMC cells based on predictive analytics
M Berecibar, F Devriendt, M Dubarry, I Villarreal, N Omar, W Verbeke, ...
Journal of Power Sources 320, 239-250, 2016
Social network analysis for customer churn prediction
W Verbeke, D Martens, B Baesens
Applied Soft Computing 14, 431-446, 2014
A novel profit maximizing metric for measuring classification performance of customer churn prediction models
T Verbraken, W Verbeke, B Baesens
IEEE transactions on knowledge and data engineering 25 (5), 961-973, 2012
Performance of classification models from a user perspective
D Martens, J Vanthienen, W Verbeke, B Baesens
Decision Support Systems 51 (4), 782-793, 2011
Credit scoring for microfinance: is it worth it?
J Van Gool, W Verbeke, P Sercu, B Baesens
International Journal of Finance & Economics 17 (2), 103-123, 2012
A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions
C De Cauwer, W Verbeke, T Coosemans, S Faid, J Van Mierlo
Energies 10 (5), 608, 2017
A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics
F Devriendt, D Moldovan, W Verbeke
Big data 6 (1), 13-41, 2018
Conventional, hybrid, or electric vehicles: which technology for an urban distribution centre?
P Lebeau, C De Cauwer, J Van Mierlo, C Macharis, W Verbeke, ...
The Scientific World Journal 2015, 2015
Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
M Óskarsdóttir, C Bravo, W Verbeke, C Sarraute, B Baesens, ...
Expert Systems with Applications 85, 204-220, 2017
Uplift Modeling for preventing student dropout in higher education
D Olaya, J Vásquez, S Maldonado, J Miranda, W Verbeke
Decision Support Systems 134, 113320, 2020
Why you should stop predicting customer churn and start using uplift models
F Devriendt, J Berrevoets, W Verbeke
Information Sciences 548, 497-515, 2021
Profit optimizing customer churn prediction with Bayesian network classifiers
T Verbraken, W Verbeke, B Baesens
Intelligent Data Analysis 18 (1), 3-24, 2014
A model for range estimation and energy-efficient routing of electric vehicles in real-world conditions
C De Cauwer, W Verbeke, J Van Mierlo, T Coosemans
IEEE Transactions on Intelligent Transportation Systems 21 (7), 2787-2800, 2019
A survey and benchmarking study of multitreatment uplift modeling
D Olaya, K Coussement, W Verbeke
Data Mining and Knowledge Discovery 34, 273-308, 2020
Instance-dependent cost-sensitive learning for detecting transfer fraud
S Höppner, B Baesens, W Verbeke, T Verdonck
European Journal of Operational Research 297 (1), 291-300, 2022
Predicting online channel acceptance with social network data
T Verbraken, F Goethals, W Verbeke, B Baesens
Decision Support Systems 63, 104-114, 2014
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