| 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 | 257 | 2014 |
| On-line elimination of local redundancies in evolving fuzzy systems E Lughofer, JL Bouchot, A Shaker Evolving systems 2 (3), 165-187, 2011 | 100 | 2011 |
| IBLStreams: a system for instance-based classification and regression on data streams A Shaker, E Hüllermeier Evolving Systems 3 (4), 235-249, 2012 | 80 | 2012 |
| Self-adaptive and local strategies for a smooth treatment of drifts in data streams A Shaker, E Lughofer Evolving Systems 5 (4), 239-257, 2014 | 59 | 2014 |
| Evolving fuzzy pattern trees for binary classification on data streams A Shaker, R Senge, E Hüllermeier Information Sciences 220, 34-45, 2013 | 36 | 2013 |
| SciPlore Xtract: extracting titles from scientific PDF documents by analyzing style information (Font Size) J Beel, B Gipp, A Shaker, N Friedrich International Conference on Theory and Practice of Digital Libraries, 413-416, 2010 | 30 | 2010 |
| Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study A Shaker, E Hüllermeier Neurocomputing 150, 250-264, 2015 | 28 | 2015 |
| Z? liobaite, I G Krempl BrzeziÁski, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A …, 2014 | 20 | 2014 |
| Instance-based classification and regression on data streams A Shaker, E Hüllermeier Learning in Non-Stationary Environments, 185-201, 2012 | 17 | 2012 |
| Imprecise matching of requirements specifications for software services using fuzzy logic MC Platenius, A Shaker, M Becker, E Huellermeier, W Schaefer IEEE Transactions on Software Engineering 43 (8), 739-759, 2016 | 10 | 2016 |
| Metabags: Bagged meta-decision trees for regression J Khiari, L Moreira-Matias, A Shaker, B Ženko, S Džeroski Joint european conference on machine learning and knowledge discovery in …, 2018 | 9 | 2018 |
| Resolving global and local drifts in data stream regression using evolving rule-based models A Shaker, E Lughofer 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 9-16, 2013 | 8 | 2013 |
| Recovery analysis for adaptive learning from non-stationary data streams A Shaker, E Hüllermeier Proceedings of the 8th International Conference on Computer Recognition …, 2013 | 7 | 2013 |
| Survival analysis on data streams: Analyzing temporal events in dynamically changing environments A Shaker, E Hüllermeier International Journal of Applied Mathematics and Computer Science 24 (1), 2014 | 6 | 2014 |
| Efficient and scalable multi-task regression on massive number of tasks X He, F Alesiani, A Shaker Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3763-3770, 2019 | 5 | 2019 |
| Learning TSK fuzzy rules from data streams A Shaker, W Heldt, E Hüllermeier Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 2 | 2017 |
| Towards Interpretable Multi-Task Learning Using Bilevel Programming F Alesiani, S Yu, A Shaker, W Yin arXiv preprint arXiv:2009.05483, 2020 | 1 | 2020 |
| Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications S Yu, A Shaker, F Alesiani, JC Principe arXiv preprint arXiv:2005.02196, 2020 | 1 | 2020 |
| Hazard analysis on data streams A Shaker, E Hüllermeier Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6.-7 …, 2014 | 1 | 2014 |
| Event history analysis on data streams: An application to earthquake occurrence A Shaker, E Hüllermeier Proceedings of RealStream, 43-46, 2013 | 1 | 2013 |
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
