Ensembles of Probability Estimation Trees for Customer Churn Prediction. - Koen W. De Bock; Dirk Van den Poel (IEA/AIE (2), 2010)

Ensemble classification based on generalized additive models. - Koen W. De Bock; Kristof Coussement; Dirk Van den Poel (Comput. Stat. Data Anal., 2010)

Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream Data. - Koen W. De Bock; Dirk Van den Poel (Fundam. Informaticae, 2010)

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. - Koen W. De Bock; Dirk Van den Poel (Expert Syst. Appl., 2011)

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. - Koen W. De Bock; Dirk Van den Poel (Expert Syst. Appl., 2012)

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection. - Annika Baumann; Stefan Lessmann; Kristof Coussement; Koen W. De Bock (ECIS, 2015)

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles. - Koen W. De Bock (Expert Syst. Appl., 2017)

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. - Arno De Caigny; Kristof Coussement; Koen W. De Bock (Eur. J. Oper. Res., 2018)

A framework for configuring collaborative filtering-based recommendations derived from purchase data. - Stijn Geuens; Kristof Coussement; Koen W. De Bock (Eur. J. Oper. Res., 2018)

Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis. - C. Gary Mena; Arno De Caigny; Kristof Coussement; Koen W. De Bock; Stefan Lessmann (CoRR, 2019)

Leveraging fine-grained transaction data for customer life event predictions. - Arno De Caigny; Kristof Coussement; Koen W. De Bock (Decis. Support Syst., 2020)

Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach. - Koen W. De Bock; Kristof Coussement; Stefan Lessmann (Eur. J. Oper. Res., 2020)

Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling. - Koen W. De Bock; Arno De Caigny (Decis. Support Syst., 2021)

Targeting customers for profit: An ensemble learning framework to support marketing decision-making. - Stefan Lessmann; Johannes Haupt; Kristof Coussement; Koen W. De Bock (Inf. Sci., 2021)

A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer. - Kristof Coussement; Koen W. De Bock; Stijn Geuens (Ann. Oper. Res., 2022)

Coupling Neural Networks Between Clusters for Better Personalized Care. - Mathias Kraus; Nico Hambauer; Kristina Müller; Pavlina Kröckel; Nalika Ulapane; Arno De Caigny; Koen W. De Bock; Kristof Coussement (HICSS, 2024)

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis. - Arno De Caigny; Koen W. De Bock; Sam Verboven (Decis. Support Syst., 2024)

Explainable Analytics for Operational Research. - Koen W. De Bock; Kristof Coussement; Arno De Caigny (Eur. J. Oper. Res., 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; et al. (Eur. J. Oper. Res., 2024)

Exploiting time-varying RFM measures for customer churn prediction with deep neural networks. - C. Gary Mena; Kristof Coussement; Koen W. De Bock; Arno De Caigny; Stefan Lessmann (Ann. Oper. Res., 2024)

Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification. - Zhenkun Liu; Koen W. De Bock; Lifang Zhang (Eur. J. Oper. Res., 2025)