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Wouter Verbeke
Researcher at Katholieke Universiteit Leuven
Publications - 93
Citations - 2541
Wouter Verbeke is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Analytics. The author has an hindex of 17, co-authored 77 publications receiving 1895 citations. Previous affiliations of Wouter Verbeke include Vrije Universiteit Brussel & University of Edinburgh.
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New insights into churn prediction in the telecommunication sector: a profit driven data mining approach
TL;DR: A novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign.
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Building comprehensible customer churn prediction models with advanced rule induction techniques
TL;DR: Two novel data mining techniques, AntMiner+ and ALBA, are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER to induce accurate as well as comprehensible classification rule-sets.
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Data Mining Techniques for Software Effort Estimation: A Comparative Study
TL;DR: A large scale benchmarking study is reported on, finding that by selecting a subset of highly predictive attributes such as project size, development, and environment related attributes, typically a significant increase in estimation accuracy can be obtained.
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Online state of health estimation on NMC cells based on predictive analytics
Maitane Berecibar,Floris Devriendt,Matthieu Dubarry,I. Villarreal,Noshin Omar,Wouter Verbeke,Joeri Van Mierlo +6 more
TL;DR: An extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability.
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Social network analysis for customer churn prediction
TL;DR: A significant impact of social network effects, including non-Markovian effects, on the performance of a customer churn prediction model is found, and the parallel model setup is shown to boost the profits generated by a retention campaign.