Publikationen

What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI)
Renftle, M.; Trittenbach, H.; Poznic, M.; Heil, R.
2024. Humanities and Social Sciences Communications, 11 (1), Art.-Nr.: 760. doi:10.1057/s41599-024-03277-x  

An Empirical Evaluation of Constrained Feature Selection
Bach, J.; Zoller, K.; Trittenbach, H.; Schulz, K.; Böhm, K.
2022. SN Computer Science, 3 (6), Art.-Nr.: 445. doi:10.1007/s42979-022-01338-z  

Efficient SVDD sampling with approximation guarantees for the decision boundary
Englhardt, A.; Trittenbach, H.; Kottke, D.; Sick, B.; Böhm, K.
2022. doi:10.48550/arXiv.2009.13853 

Efficient SVDD sampling with approximation guarantees for the decision boundary
Englhardt, A.; Trittenbach, H.; Kottke, D.; Sick, B.; Böhm, K.
2022. Machine Learning, 111 (4), 1349–1375. doi:10.1007/s10994-022-06149-0  

An overview and a benchmark of active learning for outlier detection with one-class classifiers
Trittenbach, H.; Englhardt, A.; Böhm, K.
2021. Expert systems with applications, 168, Art. Nr.: 114372. doi:10.1016/j.eswa.2020.114372 

Evaluating the Effect of XAI on Understanding of Machine Learning Models
Renftle, M.; Trittenbach, H.; Müssener, C.; Böhm, K.; Poznic, M.; Heil, R.
2021. Philosophy of Science meets Machine Learning (2021), Tübingen, Deutschland, 9.–12. November 2021 

User-Centric Active Learning for Outlier Detection. Dissertation
Trittenbach, H.
2020, März 2. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000117443  

Active Learning of SVDD Hyperparameter Values
Trittenbach, H.; Böhm, K.; Assent, I.
2020. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics: 6-9 Ocotber 2020, Sydney, Australia. Ed.: G. Webb, 109–117, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA49011.2020.00023 

Finding the sweet spot: Batch selection for one-class active learning
Englhardt, A.; Trittenbach, H.; Vetter, D.; Böhm, K.
2020. Proceedings of the 2020 SIAM International Conference on Data Mining. Ed.: C. Demeniconi, 118–126, SIAM. doi:10.1137/1.9781611976236.14 

Active Learning of SVDD Hyperparameter Values
Trittenbach, H.; Böhm, K.; Assent, I.
2019 

High-resolution Industrial Production Energy (HIPE) | Version 1.0.1
Bischof, S.; Trittenbach, H.; Werle, D.; Blank, T.; Böhm, K.
2019, Mai 1. doi:10.5281/zenodo.14054902 

One-class active learning for outlier detection with multiple subspaces
Trittenbach, H.; Böhm, K.
2019. CIKM ’19 Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, November 3-7, 2019, 811–820, Association for Computing Machinery (ACM). doi:10.1145/3357384.3357873 

Understanding the effects of temporal energy-data aggregation on clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2019. Information technology, 61 (2-3), 111–123. doi:10.1515/itit-2019-0014 

Validating one-class active learning with user studies – A prototype and open challenges
Trittenbach, H.; Englhardt, A.; Böhm, K.
2019. IAL 2019 Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany, September 16th, 2019. Ed.: D. Kottke, 17–31  

Data-driven crack assessment based on surface measurements
Schulz, K.; Kreis, S.; Trittenbach, H.; Böhm, K.
2019. Engineering fracture mechanics, 218, Article no: 106552. doi:10.1016/j.engfracmech.2019.106552 

The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving
Werle, D.; Warzel, D.; Bischof, S.; Koziolek, A.; Trittenbach, H.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 482–485, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331023 

Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions
Vollmer, M.; Englhardt, A.; Trittenbach, H.; Bielski, P.; Karrari, S.; Böhm, K.
2019. Tenth ACM International Conference on Future Energy Systems (ACM e-Energy) and its co-located workshops, Phoenix, AZ, United States, 25th - 28th of June 2019, 474–481, Association for Computing Machinery (ACM). doi:10.1145/3307772.3331022 

High-resolution Industrial Production Energy (HIPE) | Version 1.0.0
Bischof, S.; Trittenbach, H.; Werle, D.; Blank, T.; Böhm, K.
2018, Juni 12. doi:10.5281/zenodo.14054775 

Towards Simulation-Data Science : A Case Study on Material Failures
Trittenbach, H.; Gauch, M.; Böhm, K.; Schulz, K.
2018. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 1-3 Oct. 2018, 450–459, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSAA.2018.00058 

An Overview and a Benchmark of Active Learning for One-Class Classification
Trittenbach, H.; Englhardt, A.; Böhm, K.
2018. arXiv preprint 1808.04759 

HIPE – An energy-Status-Data set from industrial production
Bischof, S.; Trittenbach, H.; Vollmer, M.; Werle, D.; Blank, T.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 599–603, Association for Computing Machinery (ACM). doi:10.1145/3208903.3210278 

On the tradeoff between energy data aggregation and clustering quality
Trittenbach, H.; Bach, J.; Böhm, K.
2018. 9th ACM International Conference on Future Energy Systems, e-Energy 2018; Karlsruhe; Germany; 12 June 2018 through 15 June 2018, 399–401, Association for Computing Machinery (ACM). doi:10.1145/3208903.3212038 

Dimension-based subspace search for outlier detection
Trittenbach, H.; Böhm, K.
2018. International Journal of Data Science and Analytics. doi:10.1007/s41060-018-0137-7