| Open challenges for data stream mining research G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ... ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014 | 420 | 2014 |
| Optimised probabilistic active learning (OPAL) For fast, non-myopic, cost-sensitive active classification G Krempl, D Kottke, V Lemaire Machine Learning 100 (2-3), 449-476, 2015 | 58 | 2015 |
| Drift mining in data: A framework for addressing drift in classification V Hofer, G Krempl Computational Statistics & Data Analysis 57 (1), 377-391, 2013 | 56 | 2013 |
| Transfer learning for time series anomaly detection V Vercruyssen, W Meert, J Davis, G Krempl, V Lemaire, R Polikar, B Sick, ... Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017 | 52 | 2017 |
| The algorithm APT to classify in concurrence of latency and drift G Krempl International Symposium on Intelligent Data Analysis, 222-233, 2011 | 46 | 2011 |
| Challenges of reliable, realistic and comparable active learning evaluation D Kottke, A Calma, D Huseljic, GM Krempl, B Sick Proceedings of the workshop and tutorial on interactive adaptive learning, 2-14, 2017 | 43 | 2017 |
| Classification in presence of drift and latency G Krempl, V Hofer 2011 IEEE 11th International Conference on Data Mining Workshops, 596-603, 2011 | 38 | 2011 |
| Multi-class probabilistic active learning D Kottke, G Krempl, D Lang, J Teschner, M Spiliopoulou ECAI 2016, 586-594, 2016 | 35 | 2016 |
| Probabilistic active learning in datastreams D Kottke, G Krempl, M Spiliopoulou International Symposium on Intelligent Data Analysis, 145-157, 2015 | 35 | 2015 |
| Toward optimal probabilistic active learning using a Bayesian approach D Kottke, M Herde, C Sandrock, D Huseljic, G Krempl, B Sick Machine learning 110 (6), 1199-1231, 2021 | 33 | 2021 |
| Correcting the usage of the hoeffding inequality in stream mining P Matuszyk, G Krempl, M Spiliopoulou International Symposium on Intelligent Data Analysis, 298-309, 2013 | 32 | 2013 |
| I. ˇZliobaite, D G Krempl Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S …, 2014 | 31 | 2014 |
| Stream-based active learning for sliding windows under the influence of verification latency T Pham, D Kottke, G Krempl, B Sick Machine Learning 111 (6), 2011-2036, 2022 | 29 | 2022 |
| Probabilistic active learning: Towards combining versatility, optimality and efficiency G Krempl, D Kottke, M Spiliopoulou International Conference on Discovery Science, 168-179, 2014 | 24 | 2014 |
| Online clustering of high-dimensional trajectories under concept drift G Krempl, ZF Siddiqui, M Spiliopoulou Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 18 | 2011 |
| Frontiers in artificial intelligence and applications P Wang, Q Wang, S Jin, W Long, L Hu IOS Press. chapter What do you mean by AI 171 (1), 362-373, 2008 | 17 | 2008 |
| Probabilistic active learning for active class selection D Kottke, G Krempl, M Stecklina, CS von Rekowski, T Sabsch, TP Minh, ... arXiv preprint arXiv:2108.03891, 2021 | 16 | 2021 |
| Clustering-based optimised probabilistic active learning (COPAL) G Krempl, TC Ha, M Spiliopoulou International conference on discovery science, 101-115, 2015 | 13 | 2015 |
| How to Select Information That Matters: A Comparative Study on Active Learning Strategies for Classification C Beyer, G Krempl, V Lemaire 15th ACM International Conference on Knowledge Technologies and Data-Driven …, 2015 | 11 | 2015 |
| Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings MR Berthold, A Feelders, G Krempl Springer Nature, 2020 | 10 | 2020 |
Daniel KottkeResearcher, Kassel UniversityVerified email at uni-kassel.de
