Koen W. De Bock - researchr alias

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Viewing Publication 1 - 15 from 15

2024

Coupling Neural Networks Between Clusters for Better Personalized CareMathias Kraus, Nico Hambauer, Kristina Müller, Pavlina Kröckel, Nalika Ulapane, Arno De Caigny, Koen W. De Bock, Kristof Coussement. hicss 2023: 3627-3636 [doi]

2022

A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailerKristof Coussement, Koen W. De Bock, Stijn Geuens. anor, 315(2):671-694, 2022. [doi]

2021

Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modelingKoen W. De Bock, Arno De Caigny. dss, 150:113523, 2021. [doi]

Targeting customers for profit: An ensemble learning framework to support marketing decision-makingStefan Lessmann, Johannes Haupt, Kristof Coussement, Koen W. De Bock. isci, 557:286-301, 2021. [doi]

2020

Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approachKoen W. De Bock, Kristof Coussement, Stefan Lessmann. eor, 285(2):612-630, 2020. [doi]

Leveraging fine-grained transaction data for customer life event predictionsArno De Caigny, Kristof Coussement, Koen W. De Bock. dss, 130:113232, 2020. [doi]

2018

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision treesArno De Caigny, Kristof Coussement, Koen W. De Bock. eor, 269(2):760-772, 2018. [doi]

A framework for configuring collaborative filtering-based recommendations derived from purchase dataStijn Geuens, Kristof Coussement, Koen W. De Bock. eor, 265(1):208-218, 2018. [doi]

2017

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensemblesKoen W. De Bock. eswa, 90:23-39, 2017. [doi]

2015

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble SelectionAnnika Baumann, Stefan Lessmann, Kristof Coussement, Koen W. De Bock. ecis 2015: [doi]

2012

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive modelsKoen W. De Bock, Dirk Van den Poel. eswa, 39(8):6816-6826, 2012. [doi]

2011

An empirical evaluation of rotation-based ensemble classifiers for customer churn predictionKoen W. De Bock, Dirk Van den Poel. eswa, 38(10):12293-12301, 2011. [doi]

2010

Ensembles of Probability Estimation Trees for Customer Churn PredictionKoen W. De Bock, Dirk Van den Poel. ieaaie 2010: 57-66 [doi]

Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream DataKoen W. De Bock, Dirk Van den Poel. FUIN, 98(1):49-70, 2010. [doi]

Ensemble classification based on generalized additive modelsKoen W. De Bock, Kristof Coussement, Dirk Van den Poel. csda, 54(6):1535-1546, 2010. [doi]

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