| An overview and a benchmark of active learning for outlier detection with one-class classifiers H Trittenbach, A Englhardt, K Böhm Expert Systems with Applications 168, 114372, 2021 | 45 | 2021 |
| HIPE: An energy-status-data set from industrial production S Bischof, H Trittenbach, M Vollmer, D Werle, T Blank, K Böhm Proceedings of the Ninth International Conference on Future Energy Systems …, 2018 | 34 | 2018 |
| Dimension-based subspace search for outlier detection H Trittenbach, K Böhm International Journal of Data Science and Analytics 7 (2), 87-101, 2019 | 21 | 2019 |
| One-Class Active Learning for Outlier Detection with Multiple Subspaces H Trittenbach, K Böhm Proceedings of the 28th ACM International Conference on Information and …, 2019 | 13 | 2019 |
| Validating One-Class Active Learning with User Studies–a Prototype and Open Challenges H Trittenbach, A Englhardt, K Böhm ECML PKDD Workshop, 2019 | 11 | 2019 |
| Active Learning of SVDD Hyperparameter Values H Trittenbach, K Böhm, I Assent 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020 | 8 | 2020 |
| Finding the Sweet Spot: Batch Selection for One-Class Active Learning A Englhardt, H Trittenbach, D Vetter, K Böhm Proceedings of the 2020 SIAM International Conference on Data Mining, 118-126, 2020 | 7 | 2020 |
| Towards Simulation-Data Science–A Case Study on Material Failures H Trittenbach, M Gauch, K Böhm, K Schulz 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018 | 5 | 2018 |
| On the Tradeoff between Energy Data Aggregation and Clustering Quality H Trittenbach, J Bach, K Böhm Proceedings of the Ninth International Conference on Future Energy Systems …, 2018 | 5 | 2018 |
| Efficient SVDD sampling with approximation guarantees for the decision boundary A Englhardt, H Trittenbach, D Kottke, B Sick, K Böhm Machine Learning, 1-27, 2022 | 4 | 2022 |
| Data-driven crack assessment based on surface measurements K Schulz, S Kreis, H Trittenbach, K Böhm Engineering Fracture Mechanics 218, 106552, 2019 | 3 | 2019 |
| Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions M Vollmer, H Trittenbach, S Karrari, A Englhardt, P Bielski, K Böhm Proceedings of the Tenth ACM International Conference on Future Energy …, 2019 | 3 | 2019 |
| User-Centric Active Learning for Outlier Detection H Trittenbach Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2020, 2020 | 2 | 2020 |
| The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving D Werle, S Bischof, H Trittenbach, D Warzel, A Koziolek, K Böhm Proceedings of the Tenth ACM International Conference on Future Energy …, 2019 | 2 | 2019 |
| Understanding the effects of temporal energy-data aggregation on clustering quality H Trittenbach, J Bach, K Böhm it-Information Technology 61 (2-3), 111-123, 2019 | 2 | 2019 |
| An Empirical Evaluation of Constrained Feature Selection J Bach, K Zoller, H Trittenbach, K Schulz, K Böhm SN Computer Science 3 (6), 445, 2022 | 1 | 2022 |
| Explaining Any ML Model?--On Goals and Capabilities of XAI M Renftle, H Trittenbach, M Poznic, R Heil arXiv preprint arXiv:2206.13888, 2022 | 1 | 2022 |
| Selecting Models based on the Risk of Damage Caused by Adversarial Attacks J Klemenc, H Trittenbach arXiv preprint arXiv:2301.12151, 2023 | | 2023 |
Klemens BöhmUniv.-Prof. Dr.-Ing., IPD - Institute of Program Structures and Data OrganizationVerified email at kit.edu
