PUBLIKATIONSSERVER

Martin Kohlhase

https://orcid.org/0009-0002-9374-0720

40 Publikationen

Alle markieren

[40]
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 | DOI | Download (ext.)
 
[39]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6371 | OA
Dockhorn, F.-M., & Kohlhase, M. (2025). Discrepancy Modeling for Dynamical Systems. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 35. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/IR/1000186052
HSBI-PUB | DOI | Download (ext.)
 
[37]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
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 | Dateien verfügbar | DOI
 
[36]
2025 | Artikel | FH-PUB-ID: 6297
Katter, V., Huperz, C., & Kohlhase, M. (2025). Sensorintegration in Orthesen zur Versorgung des Diabetischen Fußsyndroms: eine technische Betrachtung. Orthopädie Technik, (11), 68–73.
HSBI-PUB | Download (ext.)
 
[35]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6298 | OA
Katter, V., & Kohlhase, M. (2025). Efficient Gait Analysis using Knowledge Distillation from Sparse Sensors. In H. Schulte, F. Hoffmann, R. Mikut, & Karlsruher Institut für Technologie (KIT) (Eds.), Proceedings – 35. Workshop Computational Intelligence: Berlin, 20.–21. November 2025 (pp. 89–96). Karlsruhe: Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/IR/1000186052
HSBI-PUB | DOI | Download (ext.)
 
[34]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster, B., & Kohlhase, M. (2025). Trust Issues in Active Learning and Their Impact on Real-World Applications. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion) (pp. 1–5). Trondheim, Norway: IEEE. https://doi.org/10.1109/CITRExCompanion65208.2025.10981492
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[33]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
Jaster, B., & Kohlhase, M. (2025). Trust Issues in Active Learning and Their Impact on Real-World Applications. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion) (pp. 1–5). Trondheim, Norway: IEEE. https://doi.org/10.57720/6045
HSBI-PUB | Dateien verfügbar | DOI
 
[32]
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
 
[31]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
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 | DOI | Download (ext.)
 
[30]
2025 | Artikel | FH-PUB-ID: 6268 | OA
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.)
 
[29]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789 | OA
Dockhorn, F.-M., & Kohlhase, M. (2024). An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 53–76). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000174544
HSBI-PUB | DOI | Download (ext.)
 
[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
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
HSBI-PUB | DOI | Download (ext.)
 
[27]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[26]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[25]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[24]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
HSBI-PUB | DOI
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Jaster, B., & Kohlhase, M. (2023). Active Learning for Regression Problems with Ensemble Methods. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 33. Workshop Computational Intelligence (pp. 9–29). Berlin: Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/KSP/1000162754
HSBI-PUB | DOI | Download (ext.)
 
[22]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper, L., Grumbach, F., Kohlhase, M., & Reusch, P. (2023). Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. Wt Werkstattstechnik Online, 113(04), 158–163. https://doi.org/10.37544/1436-4980-2023-04-58
HSBI-PUB | DOI | Download (ext.)
 
[21]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[20]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[19]
2023 | Artikel | FH-PUB-ID: 2855 | OA
Vollenkemper, L., Mönikes, M., Wortmann, F., Rudolph-Puls, M., Kohlhase, M., Röchter, A., & Ewering, C. (2023). HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems. Arbeitswelt.Plus Working Paper. https://doi.org/10.55594/UXIT4205
HSBI-PUB | DOI | Download (ext.)
 
[18]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors, 22(7). https://doi.org/10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[17]
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
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
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.)
 
[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
Vollenkemper, L., & Kohlhase, M. (2022). Spatial Temporal Transformer Networks for Sparse Motion Capture Applications. In H. Schulte, F. Hoffman, R. Mikut, & Karlsruhier Institut für Technologie (KIT) (Eds.), PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE (Vol. 32). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000151141
HSBI-PUB | DOI | Download (ext.)
 
[14]
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 | Download (ext.)
 
[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
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 | DOI | Download (ext.)
 
[12]
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 | DOI | Download (ext.)
 
[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
Voigt, T., Kohlhase, M., & Nelles, O. (2021). Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics, 9(19). https://doi.org/10.3390/math9192479
HSBI-PUB | DOI | Download (ext.)
 
[10]
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
 
[9]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[8]
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.)
 
[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
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 | DOI | Download (ext.)
 
[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
Godt, S., & Kohlhase, M. (2020). Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence (pp. 159–180). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt, T., Kohlhase, M., & Nelles, O. (2020). Incremental Latin Hypercube Additive Design for LOLIMOT. In Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1602–1609). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212173
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
Voigt, T., Kohlhase, M., & Peter, A. (2020). Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin, 62(04), 62–69.
HSBI-PUB
 
[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
Voigt, T., Kohlhase, M., & Nelles, O. (2019). Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In Proceedings - 29. Workshop Computational Intelligence (pp. 267–288). Dortmund: KIT Scientific Publishing, Karlsruhe. https://doi.org/10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
Godt, S., & Kohlhase, M. (2019). Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence (pp. 51–72). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000098736
HSBI-PUB | DOI | Download (ext.)
 
[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
Voigt, T., & Kohlhase, M. (2018). Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In Proceedings - 28. Workshop Computational Intelligence (pp. 93–111). KIT Scientific Publishing, Karlsruhe.
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40 Publikationen

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[40]
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 | DOI | Download (ext.)
 
[39]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6371 | OA
Dockhorn, F.-M., & Kohlhase, M. (2025). Discrepancy Modeling for Dynamical Systems. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 35. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/IR/1000186052
HSBI-PUB | DOI | Download (ext.)
 
[37]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
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 | Dateien verfügbar | DOI
 
[36]
2025 | Artikel | FH-PUB-ID: 6297
Katter, V., Huperz, C., & Kohlhase, M. (2025). Sensorintegration in Orthesen zur Versorgung des Diabetischen Fußsyndroms: eine technische Betrachtung. Orthopädie Technik, (11), 68–73.
HSBI-PUB | Download (ext.)
 
[35]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6298 | OA
Katter, V., & Kohlhase, M. (2025). Efficient Gait Analysis using Knowledge Distillation from Sparse Sensors. In H. Schulte, F. Hoffmann, R. Mikut, & Karlsruher Institut für Technologie (KIT) (Eds.), Proceedings – 35. Workshop Computational Intelligence: Berlin, 20.–21. November 2025 (pp. 89–96). Karlsruhe: Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/IR/1000186052
HSBI-PUB | DOI | Download (ext.)
 
[34]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster, B., & Kohlhase, M. (2025). Trust Issues in Active Learning and Their Impact on Real-World Applications. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion) (pp. 1–5). Trondheim, Norway: IEEE. https://doi.org/10.1109/CITRExCompanion65208.2025.10981492
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[33]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
Jaster, B., & Kohlhase, M. (2025). Trust Issues in Active Learning and Their Impact on Real-World Applications. In IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion) (pp. 1–5). Trondheim, Norway: IEEE. https://doi.org/10.57720/6045
HSBI-PUB | Dateien verfügbar | DOI
 
[32]
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
 
[31]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
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 | DOI | Download (ext.)
 
[30]
2025 | Artikel | FH-PUB-ID: 6268 | OA
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.)
 
[29]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789 | OA
Dockhorn, F.-M., & Kohlhase, M. (2024). An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 53–76). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000174544
HSBI-PUB | DOI | Download (ext.)
 
[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
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
HSBI-PUB | DOI | Download (ext.)
 
[27]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[26]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[25]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[24]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
HSBI-PUB | DOI
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Jaster, B., & Kohlhase, M. (2023). Active Learning for Regression Problems with Ensemble Methods. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 33. Workshop Computational Intelligence (pp. 9–29). Berlin: Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/KSP/1000162754
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[22]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper, L., Grumbach, F., Kohlhase, M., & Reusch, P. (2023). Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. Wt Werkstattstechnik Online, 113(04), 158–163. https://doi.org/10.37544/1436-4980-2023-04-58
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[21]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[20]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
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[19]
2023 | Artikel | FH-PUB-ID: 2855 | OA
Vollenkemper, L., Mönikes, M., Wortmann, F., Rudolph-Puls, M., Kohlhase, M., Röchter, A., & Ewering, C. (2023). HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems. Arbeitswelt.Plus Working Paper. https://doi.org/10.55594/UXIT4205
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[18]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors, 22(7). https://doi.org/10.3390/s22072481
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[17]
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
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
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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[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
Vollenkemper, L., & Kohlhase, M. (2022). Spatial Temporal Transformer Networks for Sparse Motion Capture Applications. In H. Schulte, F. Hoffman, R. Mikut, & Karlsruhier Institut für Technologie (KIT) (Eds.), PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE (Vol. 32). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000151141
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[14]
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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[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
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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[12]
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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[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
Voigt, T., Kohlhase, M., & Nelles, O. (2021). Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics, 9(19). https://doi.org/10.3390/math9192479
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[10]
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
 
[9]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[8]
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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[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
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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[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
Godt, S., & Kohlhase, M. (2020). Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence (pp. 159–180). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt, T., Kohlhase, M., & Nelles, O. (2020). Incremental Latin Hypercube Additive Design for LOLIMOT. In Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1602–1609). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212173
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
Voigt, T., Kohlhase, M., & Peter, A. (2020). Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin, 62(04), 62–69.
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[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
Voigt, T., Kohlhase, M., & Nelles, O. (2019). Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In Proceedings - 29. Workshop Computational Intelligence (pp. 267–288). Dortmund: KIT Scientific Publishing, Karlsruhe. https://doi.org/10.5445/KSP/1000098736
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[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
Godt, S., & Kohlhase, M. (2019). Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence (pp. 51–72). Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000098736
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[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
Voigt, T., & Kohlhase, M. (2018). Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In Proceedings - 28. Workshop Computational Intelligence (pp. 93–111). KIT Scientific Publishing, Karlsruhe.
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