| 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 | 570 | 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 | 485 | 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 | 340 | 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 | 279 | 2011 |
| Social network analysis for customer churn prediction W Verbeke, D Martens, B Baesens Applied Soft Computing 14, 431-446, 2014 | 200 | 2014 |
| 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 | 195 | 2016 |
| 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 | 191 | 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 | 166 | 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 | 158 | 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 | 148 | 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 | 139 | 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 (1), 302867, 2015 | 119 | 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 | 112 | 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 | 105 | 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 | 94 | 2021 |
| Profit optimizing customer churn prediction with Bayesian network classifiers T Verbraken, W Verbeke, B Baesens Intelligent Data Analysis 18 (1), 3-24, 2014 | 55 | 2014 |
| 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 | 50 | 2022 |
| A survey and benchmarking study of multitreatment uplift modeling D Olaya, K Coussement, W Verbeke Data Mining and Knowledge Discovery 34, 273-308, 2020 | 49 | 2020 |
| 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 | 49 | 2019 |
| Predicting online channel acceptance with social network data T Verbraken, F Goethals, W Verbeke, B Baesens Decision Support Systems 63, 104-114, 2014 | 46 | 2014 |
Bart BaesensFull professor, KU Leuven在 kuleuven.be 的电子邮件经过验证
