Wouter Verbeke

3.7k total citations
76 papers, 2.6k citations indexed

About

Wouter Verbeke is a scholar working on Artificial Intelligence, Marketing and Information Systems. According to data from OpenAlex, Wouter Verbeke has authored 76 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 24 papers in Marketing and 20 papers in Information Systems. Recurrent topics in Wouter Verbeke's work include Customer churn and segmentation (22 papers), Imbalanced Data Classification Techniques (19 papers) and Data Mining Algorithms and Applications (13 papers). Wouter Verbeke is often cited by papers focused on Customer churn and segmentation (22 papers), Imbalanced Data Classification Techniques (19 papers) and Data Mining Algorithms and Applications (13 papers). Wouter Verbeke collaborates with scholars based in Belgium, United Kingdom and France. Wouter Verbeke's co-authors include Bart Baesens, David Martens, Karel Dejaeger, Joeri Van Mierlo, David Martens, Joon Hur, Christophe Mues, Thomas Verbraken, Véronique Van Vlasselaer and Cedric De Cauwer and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Power Sources and European Journal of Operational Research.

In The Last Decade

Wouter Verbeke

68 papers receiving 2.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Wouter Verbeke 917 671 608 377 376 76 2.6k
Stephen Shaoyi Liao 282 0.3× 871 1.3× 660 1.1× 71 0.2× 183 0.5× 120 2.6k
Guido Perboli 352 0.4× 137 0.2× 482 0.8× 1.0k 2.7× 141 0.4× 137 3.7k
Ramayya Krishnan 864 0.9× 741 1.1× 791 1.3× 107 0.3× 119 0.3× 155 3.6k
Wei Xu 264 0.3× 1.0k 1.5× 646 1.1× 48 0.1× 71 0.2× 153 3.0k
Minghe Sun 297 0.3× 317 0.5× 208 0.3× 69 0.2× 198 0.5× 140 2.4k
Rusli Abdullah 482 0.5× 323 0.5× 521 0.9× 80 0.2× 61 0.2× 201 2.2k
Stefan Lessmann 634 0.7× 1.8k 2.7× 1.3k 2.2× 33 0.1× 177 0.5× 100 4.5k
George M. Giaglis 456 0.5× 203 0.3× 769 1.3× 51 0.1× 185 0.5× 121 2.6k
Rakesh Vohra 736 0.8× 433 0.6× 129 0.2× 216 0.6× 133 0.4× 130 3.9k

Countries citing papers authored by Wouter Verbeke

Since Specialization
Citations

This map shows the geographic impact of Wouter Verbeke's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wouter Verbeke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wouter Verbeke more than expected).

Fields of papers citing papers by Wouter Verbeke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wouter Verbeke. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wouter Verbeke. The network helps show where Wouter Verbeke may publish in the future.

Co-authorship network of co-authors of Wouter Verbeke

This figure shows the co-authorship network connecting the top 25 collaborators of Wouter Verbeke. A scholar is included among the top collaborators of Wouter Verbeke based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wouter Verbeke. Wouter Verbeke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lessmann, Stefan, et al.. (2025). Uplift modeling with continuous treatments: A predict-then-optimize approach. European Journal of Operational Research. 330(1). 230–244.
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López-Ospina, Héctor, et al.. (2025). A prescriptive analytics framework for jointly optimizing retention incentives and targeting. Knowledge-Based Systems. 330. 114649–114649.
4.
Verbeke, Wouter, et al.. (2025). Advances in Continual Graph Learning for Anti‐Money Laundering Systems: A Comprehensive Review. Wiley Interdisciplinary Reviews Computational Statistics. 17(3).
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Verbeke, Wouter, et al.. (2024). Probabilistic Forecasting With Modified N-BEATS Networks. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18872–18885. 3 indexed citations
7.
Smedt, Johannes De, et al.. (2024). Evaluating text classification: A benchmark study. Expert Systems with Applications. 254. 124302–124302. 10 indexed citations
8.
Verbeke, Wouter, et al.. (2024). Network analytics for insurance fraud detection: a critical case study. European Actuarial Journal. 14(3). 965–990. 1 indexed citations
9.
Verdonck, Tim, et al.. (2023). Fraud analytics: A decade of research. Expert Systems with Applications. 232. 120605–120605. 9 indexed citations
10.
Bock, Koen W. De, Kristof Coussement, Arno De Caigny, et al.. (2023). Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. European Journal of Operational Research. 317(2). 249–272. 61 indexed citations
11.
Boute, Robert, et al.. (2023). Optimizing the preventive maintenance frequency with causal machine learning. International Journal of Production Economics. 258. 108798–108798. 10 indexed citations
12.
Verdonck, Tim, et al.. (2023). Robust instance-dependent cost-sensitive classification. Advances in Data Analysis and Classification. 17(4). 1057–1079. 3 indexed citations
13.
Verbeke, Wouter, et al.. (2022). Treatment effect optimisation in dynamic environments. SHILAP Revista de lepidopterología. 10(1). 106–122. 4 indexed citations
14.
Maldonado, Sebastián, et al.. (2021). Redefining profit metrics for boosting student retention in higher education. Decision Support Systems. 143. 113493–113493. 26 indexed citations
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Verbeke, Wouter, David Martens, & Bart Baesens. (2017). RULEM: rule learning with monotonicity constraints for ordinal classification. Applied Soft Computing. 1 indexed citations
17.
Óskarsdóttir, María, Cristián Bravo, Wouter Verbeke, et al.. (2016). A comparative study of social network classifiers for predicting churn in the telecommunication industry. arXiv (Cornell University). 1151–1158. 12 indexed citations
18.
Baesens, Bart, Véronique Van Vlasselaer, & Wouter Verbeke. (2015). Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection. CERN Document Server (European Organization for Nuclear Research). 78 indexed citations
19.
Verbraken, Thomas, Frank Goethals, Wouter Verbeke, & Bart Baesens. (2011). Using social network classifiers for predicting e-commerce adoption. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
20.
Verbeke, Wouter, Bart Baesens, David Martens, Manu De Backer, & Raf Haesen. (2009). Including Domain Knowledge in Customer Churn Prediction Using AntMiner. VUBIR (Vrije Universiteit Brussel). 10–21. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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