Publikationen: Gerhard Klassen
2020
| · | M. Tatusch, G. Klassen, S. Conrad: Behave or be detected! Identifying outlier sequences by their group cohesion Big Data Analytics and Knowledge Discovery, 22nd International Conference, DaWaK 2020 (to be published) | ||
| · | M. Tatusch, G. Klassen, M. Bravidor, S. Conrad: How is Your Team Spirit? Cluster Over-Time Stability Evaluation Machine Learning and Data Mining in Pattern Recognition, 16th International Conference on Machine Learning and Data Mining, MLDM 2020 |  | |
| · | G. Klassen, M. Tatusch, W. Huo, S. Conrad: Evaluating Machine Learning Algorithms in Predicting Financial Restatements ICBIM '20: Proceedings of the 4th International Conference on Business and Information Management (to be published) | ||
| · | M. Tatusch, G. Klassen, M. Bravidor, S. Conrad: Predicting Erroneous Financial Statements Using a Density-Based Clustering Approach ICBIM '20: Proceedings of the 4th International Conference on Business and Information Management (to be published) | ||
| · | G. Klassen, M. Tatusch, L. Himmelspach, S. Conrad: Fuzzy Clustering Stability Evaluation of Time Series Information Processing and Management of Uncertainty in Knowledge-Based Systems, 18th International Conference, IPMU 2020 | |
2019
| · | M. Tatusch, G. Klassen, M. Bravidor, S. Conrad: Show Me Your Friends and I'll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions Data Mining. AusDM 2019. Communications in Computer and Information Science | |
2018
| · | K. Bogomasov, L. Himmelspach, G. Klassen, M. Tatusch, S. Conrad: Feature-Based Approach for Severity Scoring of Lung Tuberculosis from CT Images Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum | |
2017
| · | G. Klassen, M. Singhof: Shape Based Outlier Detection in SLIC Superpixels Proceedings of the 29th GI-Workshop Grundlagen von Datenbanken, Blankenburg/Harz, Germany | | |
| · | M. Singhof, G. Klassen, D. Braun, S. Conrad: Detection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chains Datenbanksysteme für Business, Technologie und Web (BTW 2017) | |
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