Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability Framework.”10.1080/12460125.2023.2207268__Journal of Decision Systems, Taylor &; Francis, 2023, pp. 1–41, doi:10.1080/12460125.2023.2207268.

Maalouly, Jad, et al. “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development.” 2022 Kleinheubach Conference, IEEE, 2022.

Sander, Tom, et al. “Detection of Defects on Irregularly Structured Surfaces Using Supervised and Semi-Supervised Learning Methods.”10.1109/ssi56489.2022.9901433__2022 Smart Systems Integration (SSI), IEEE, 2022, doi:10.1109/ssi56489.2022.9901433.

Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.”https://doi.org/10.1016/j.ifacol.2022.07.282__14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24, doi:https://doi.org/10.1016/j.ifacol.2022.07.282.

Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.”10.3390/app12189094__Applied Sciences, vol. 12, no. 18, Multidisciplinary Digital Publishing Institute, 2022, p. 9094, doi:10.3390/app12189094.

Bartelheimer, Christian, et al. “Systematizing the Lexicon of Platforms in Information Systems: A Data-Driven Study.”10.1007/s12525-022-00530-6__Electronic Markets, vol. 32, Springer Science and Business Media LLC, 2022, pp. 375–96, doi:10.1007/s12525-022-00530-6.

Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for K12 Computing Education.”10.1145/3488042.3490509__21st Koli Calling International Conference on Computing Education Research, Association for Computing Machinery, 2021, doi:10.1145/3488042.3490509.

Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.

Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.”10.1109/icit46573.2021.9453646__22nd IEEE International Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453646.

Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches for Network and Service Coordination. 2021.

Schlichtig, Michael, et al. “Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material.” ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings, edited by Egl Jasut and Sergei Pozdniakov, vol. 12, 2019, pp. 65–73.

Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.”10.1007/s00180-017-0742-2__Computational Statistics, vol. 34, no. 3, 2019, pp. 977–991, doi:10.1007/s00180-017-0742-2.

Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018.

Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.”10.14257/ijast.2018.112.12__International Journal of Advanced Science and Technology, vol. 112, SERSC Australia, 2018, pp. 123–36, doi:10.14257/ijast.2018.112.12.

Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.”10.1162/evco_a_00215__Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620, doi:10.1162/evco_a_00215.

Kim, Yeong-Su, et al. “Semantic Annotation of Software Requirements with Language Frame.” International Journal of Software Engineering for Smart Device, vol. 4, no. 2, Global Vision School Publication, 2017, pp. 1–6.

Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”10.1145/2330163.2330209__Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2012, pp. 313–320, doi:10.1145/2330163.2330209.