Marvin Schöne
18 Publikationen
2025 | Buchbeitrag | FH-PUB-ID: 6273 | 
Schöne, M., Jaster, B., Bültemeier, J., & Kohlhase, M. (2025). Informed Active Learning with Decision Trees to Balance Exploration and Exploitation. In Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2025 Zukunft im Fokus – Posterbeiträge (Vol. 2, pp. 26–27). Bielefeld: Hochschule Bielefeld. https://doi.org/10.60802/sidas.2025.2
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2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier, J., Holst, C.-A., Lohweg, V., Schöne, M., Jaster, B., & Kohlhase, M. (2025). AI Workflow for Scarce Data: A Modular Approach to Optimise Processes. In 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–4). Porto, Portugal: IEEE. https://doi.org/10.1109/ETFA65518.2025.11205664
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2025 | Artikel | FH-PUB-ID: 6268 |
Schöne, M., Kohlhase, M., & Nelles, O. (2025). Incorporation of structural properties of the response surface into oblique model trees. At - Automatisierungstechnik, 73(10), 727–739. https://doi.org/10.1515/auto-2025-0017
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2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne, M., Jaster, B., Bültemeier, J., Kösters, J., Holst, C.-A., & Kohlhase, M. (2025). Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) (pp. 1–9). Trondheim, Norway: IEEE. https://doi.org/10.57720/6049
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2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne, M., Jaster, B., Bültemeier, J., Kösters, J., Holst, C.-A., & Kohlhase, M. (2025). Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) (pp. 1–9). Trondheim, Norway: IEEE. https://doi.org/10.1109/CITREx64975.2025.10974940
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2024 | Artikel | FH-PUB-ID: 6299 |
Katter, V., Jaster, B., & Schöne, M. (2024). Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme . Schriftenreihe des Institute for Data Science Solutions, 2. https://doi.org/10.60802/SIDAS.2024.2
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2024 | Buchbeitrag | FH-PUB-ID: 6272 |
Schöne, M., & Bültemeier, J. (2024). AI for Scarce Data (AI4ScaDa) — Maschinelles Lernen und Informationsfusion zur nachhaltigen Nutzung von Labor- und Kundendaten. In Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2023 Solutions im Fokus – Posterbeiträge (Vol. 1, pp. 28–29). Bielefeld: Hochschule Bielefeld. https://doi.org/10.60802/sidas.2024.1
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2024 | Konferenzbeitrag | FH-PUB-ID: 5882 |
Bültemeier, J., Schöne, M., Kohlhase, M., Holst, C.-A., Lohweg, V., & Nelles, O. (2024). Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 217–231). Berlin: KIT Scientific Publishing. https://doi.org/10.58895/ksp//1000174544-14
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, J., & Schöne, M. (n.d.). Active Learning mit dem GUIDE-Entscheidungsbaum.
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
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2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, M., & Kohlhase, M. (2021). Curvature-Oriented Splitting for Multivariate Model Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–09). Orlando, FL, USA: IEEE. https://doi.org/10.1109/SSCI50451.2021.9659858
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, J., Schöne, M., Godt, S., & Kohlhase, M. (2021). Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 31. Workshop Computational Intelligence (pp. 285–305). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000138532
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
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2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, M., & Kohlhase, M. (2020). Least Squares Approach for Multivariate Split Selection in Regression Trees. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I (pp. 41–50). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-62362-3_5
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, M., & Kohlhase, M. (2020). Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
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18 Publikationen
2025 | Buchbeitrag | FH-PUB-ID: 6273 |
Schöne, M., Jaster, B., Bültemeier, J., & Kohlhase, M. (2025). Informed Active Learning with Decision Trees to Balance Exploration and Exploitation. In Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2025 Zukunft im Fokus – Posterbeiträge (Vol. 2, pp. 26–27). Bielefeld: Hochschule Bielefeld. https://doi.org/10.60802/sidas.2025.2
HSBI-PUB
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| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier, J., Holst, C.-A., Lohweg, V., Schöne, M., Jaster, B., & Kohlhase, M. (2025). AI Workflow for Scarce Data: A Modular Approach to Optimise Processes. In 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–4). Porto, Portugal: IEEE. https://doi.org/10.1109/ETFA65518.2025.11205664
HSBI-PUB
| DOI
2025 | Artikel | FH-PUB-ID: 6268 |
Schöne, M., Kohlhase, M., & Nelles, O. (2025). Incorporation of structural properties of the response surface into oblique model trees. At - Automatisierungstechnik, 73(10), 727–739. https://doi.org/10.1515/auto-2025-0017
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne, M., Jaster, B., Bültemeier, J., Kösters, J., Holst, C.-A., & Kohlhase, M. (2025). Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) (pp. 1–9). Trondheim, Norway: IEEE. https://doi.org/10.57720/6049
HSBI-PUB
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2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne, M., Jaster, B., Bültemeier, J., Kösters, J., Holst, C.-A., & Kohlhase, M. (2025). Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) (pp. 1–9). Trondheim, Norway: IEEE. https://doi.org/10.1109/CITREx64975.2025.10974940
HSBI-PUB
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2024 | Artikel | FH-PUB-ID: 6299 |
Katter, V., Jaster, B., & Schöne, M. (2024). Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme . Schriftenreihe des Institute for Data Science Solutions, 2. https://doi.org/10.60802/SIDAS.2024.2
HSBI-PUB
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2024 | Buchbeitrag | FH-PUB-ID: 6272 |
Schöne, M., & Bültemeier, J. (2024). AI for Scarce Data (AI4ScaDa) — Maschinelles Lernen und Informationsfusion zur nachhaltigen Nutzung von Labor- und Kundendaten. In Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2023 Solutions im Fokus – Posterbeiträge (Vol. 1, pp. 28–29). Bielefeld: Hochschule Bielefeld. https://doi.org/10.60802/sidas.2024.1
HSBI-PUB
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2024 | Konferenzbeitrag | FH-PUB-ID: 5882 |
Bültemeier, J., Schöne, M., Kohlhase, M., Holst, C.-A., Lohweg, V., & Nelles, O. (2024). Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 217–231). Berlin: KIT Scientific Publishing. https://doi.org/10.58895/ksp//1000174544-14
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, J., & Schöne, M. (n.d.). Active Learning mit dem GUIDE-Entscheidungsbaum.
HSBI-PUB
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
HSBI-PUB
| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, M., & Kohlhase, M. (2021). Curvature-Oriented Splitting for Multivariate Model Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–09). Orlando, FL, USA: IEEE. https://doi.org/10.1109/SSCI50451.2021.9659858
HSBI-PUB
| DOI
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, J., Schöne, M., Godt, S., & Kohlhase, M. (2021). Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 31. Workshop Computational Intelligence (pp. 285–305). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000138532
HSBI-PUB
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
HSBI-PUB
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB
| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, M., & Kohlhase, M. (2020). Least Squares Approach for Multivariate Split Selection in Regression Trees. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I (pp. 41–50). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-62362-3_5
HSBI-PUB
| DOI
| Download (ext.)
2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, M., & Kohlhase, M. (2020). Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
HSBI-PUB
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