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Publications of Moritz Kulessa
2021
| , and , A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance (2021), in: Computers, 10:3 |
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| , , and , Combining Predictions under Uncertainty: The Case of Random Decision Trees, in: Discovery Science, pages 78--93, Springer, 2021 |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: CoRR, abs/2110.09208 |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: Frontiers in Big Data |
| , and , Revisiting Non-Specific Syndromic Surveillance (2021), in: CoRR, abs/2101.12246 |
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| , and , Revisiting Non-Specific Syndromic Surveillance, in: Advances in Intelligent Data Analysis {XIX} - 19th International Symposium on Intelligent Data Analysis, {IDA} 2021, Porto, Portugal, April 26-28, 2021, Proceedings, pages 128-140, Springer International Publishing, 2021 |
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| , , and , Sum-Product Networks for Early Outbreak Detection of Emerging Diseases, in: Artificial Intelligence in Medicine, pages 61--71, Springer International Publishing, 2021 |
| , , and , Tree-Based Dynamic Classifier Chains (2021), in: Machine Learning Journal |
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2020
| , , and , Auswertung der Ersteinschätzung-Routine-Daten unter Verwendung von statistischen Analyseverfahren zur Früherkennung von epidemischen Gefahrenlagen, in: 15. Jahrestagung Deutsche Gesellschaft Interdisziplinäre Notfall- und Akutmedizin (DGINA), 2020 |
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| , and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, Knowledge Engineering Group, Technische Universität Darmstadt, number 2006.08094 [cs.LG], ArXiv e-prints, 2020 |
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| , and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, in: Discovery Science - 23rd International Conference, {DS} 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings, pages 471--485, Springer International Publishing, 2020 |
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| , and , Improving the Fusion of Outbreak Detection Methods with Supervised Learning, in: Computational Intelligence Methods for Bioinformatics and Biostatistics - 16th International Meeting, {CIBB} 2019, Bergamo, Italy, September 4-6, 2019, Revised Selected Papers, Bergamo, Italy, pages 55--66, Springer, 2020 |
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2019
| , , , , and , DeepDB: Learn from Data, not from Queries!, arXiv preprint arXiv:1909.00607, 2019 |
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| , and , Improving Outbreak Detection with Stacking of Statistical Surveillance Methods, in: Workshop Proceedings of epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery (held in conjunction with ACM SIGKDD 2019), Anchorage, USA, 2019 |
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| , , , and , Towards Model-based Approximate Query Processing, in: Working Notes of the 1st International Workshop on Applied AI for Database Systems and Applications (held in conjunction with VLDB 2019), Los Angeles, USA, 2019 |
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2018
| and , Dynamic Classifier Chain with Random Decision Trees, in: Proceedings of the 21st International Conference of Discovery Science (DS-18), Limassol, Cyprus, pages 33--50, Springer-Verlag, 2018 |
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| , , , and , Model-based Approximate Query Processing, arXiv preprint arXiv:1811.06224, 2018 |
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2015
| , Online-Lernen von zufälligen Entscheidungsbäumen, TU Darmstadt, Knowledge Engineering Group, 2015 |
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