| 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 | 390 | 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 | 55 | 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 | 54 | 2013 |
| The algorithm APT to classify in concurrence of latency and drift G Krempl Advances in Intelligent Data Analysis X: 10th International Symposium, IDA …, 2011 | 47 | 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 | 42 | 2017 |
| Transfer Learning for Time Series Anomaly Detection. V Vercruyssen, W Meert, J Davis IAL@ PKDD/ECML, 27-36, 2017 | 42 | 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 | 34 | 2016 |
| Probabilistic active learning in datastreams D Kottke, G Krempl, M Spiliopoulou Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015 | 30 | 2015 |
| 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 | 30 | 2013 |
| I. ˇZliobaite, D G Krempl Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S …, 2014 | 28 | 2014 |
| 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 | 25 | 2021 |
| Stream-based active learning for sliding windows under the influence of verification latency T Pham, D Kottke, G Krempl, B Sick Machine Learning, 1-26, 2022 | 22 | 2022 |
| Probabilistic active learning: Towards combining versatility, optimality and efficiency G Krempl, D Kottke, M Spiliopoulou Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014 | 21 | 2014 |
| Online clustering of high-dimensional trajectories under concept drift G Krempl, ZF Siddiqui, M Spiliopoulou Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 18 | 2011 |
| 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 | 15 | 2021 |
| Clustering-based optimised probabilistic active learning (COPAL) G Krempl, TC Ha, M Spiliopoulou Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada …, 2015 | 12 | 2015 |
| H¨ ullermeier G Krempl, I Zliobaite, D Brzezinski E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M …, 2014 | 12 | 2014 |
| 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 |
| Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI) ZF Siddiqui, G Krempl, M Spiliopoulou, JM Pena, N Paul, F Maestu Brain informatics 2, 33-44, 2015 | 9 | 2015 |
Daniel KottkeResearcher, Kassel UniversityVerified email at uni-kassel.de