| Benchmarking classification models for software defect prediction: A proposed framework and novel findings S Lessmann, B Baesens, C Mues, S Pietsch IEEE transactions on software engineering 34 (4), 485-496, 2008 | 1410 | 2008 |
| Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research S Lessmann, B Baesens, HV Seow, LC Thomas European Journal of Operational Research 247 (1), 124-136, 2015 | 985 | 2015 |
| Annals of Information Systems U Apte, U Karmarkar, U Kulkarni, DJ Power, R Sharda, S Kozielski, ... | 409 | 2009 |
| The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing SF Crone, S Lessmann, R Stahlbock European Journal of Operational Research 173 (3), 781-800, 2006 | 276 | 2006 |
| A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data S Srivastava, S Lessmann Solar Energy 162, 232-247, 2018 | 245 | 2018 |
| A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry K Coussement, S Lessmann, G Verstraeten Decision Support Systems 95, 27-36, 2017 | 212 | 2017 |
| Bridging the divide in financial market forecasting: machine learners vs. financial economists MW Hsu, S Lessmann, MC Sung, T Ma, JEV Johnson Expert systems with Applications 61, 215-234, 2016 | 177 | 2016 |
| Genetic algorithms for support vector machine model selection S Lessmann, R Stahlbock, SF Crone The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006 | 169 | 2006 |
| Extreme learning machines for credit scoring: An empirical evaluation A Bequé, S Lessmann Expert Systems with Applications 86, 42-53, 2017 | 130 | 2017 |
| A reference model for customer-centric data mining with support vector machines S Lessmann, S Voß European Journal of Operational Research 199 (2), 520-530, 2009 | 113 | 2009 |
| Predicting online shopping behaviour from clickstream data using deep learning D Koehn, S Lessmann, M Schaal Expert Systems with Applications 150, 113342, 2020 | 110 | 2020 |
| Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning J Engelmann, S Lessmann Expert Systems with Applications 174, 114582, 2021 | 109 | 2021 |
| A multi-objective approach for profit-driven feature selection in credit scoring N Kozodoi, S Lessmann, K Papakonstantinou, Y Gatsoulis, B Baesens Decision support systems 120, 106-117, 2019 | 92 | 2019 |
| Spurious patterns in Google Trends data-An analysis of the effects on tourism demand forecasting in Germany B Bokelmann, S Lessmann Tourism management 75, 1-12, 2019 | 91 | 2019 |
| Deep learning for detecting financial statement fraud P Craja, A Kim, S Lessmann Decision Support Systems 139, 113421, 2020 | 84 | 2020 |
| Incorporating textual information in customer churn prediction models based on a convolutional neural network A De Caigny, K Coussement, KW De Bock, S Lessmann International Journal of Forecasting 36 (4), 1563-1578, 2020 | 79 | 2020 |
| Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting A Kim, Y Yang, S Lessmann, T Ma, MC Sung, JEV Johnson European Journal of Operational Research 283 (1), 217-234, 2020 | 77 | 2020 |
| Improving crime count forecasts using Twitter and taxi data L Vomfell, WK Härdle, S Lessmann Decision Support Systems 113, 73-85, 2018 | 73 | 2018 |
| Targeting customers for profit: An ensemble learning framework to support marketing decision-making S Lessmann, J Haupt, K Coussement, KW De Bock Information Sciences 557, 286-301, 2021 | 63 | 2021 |
| A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach A Behl, P Dutta, S Lessmann, YK Dwivedi, S Kar Information systems and e-business management 17, 285-318, 2019 | 63 | 2019 |
Sven F. CroneAssistant Professor of Business Analytics, Lancaster UniversityBestätigte E-Mail-Adresse bei lancaster.ac.uk
