HIEF: a holistic interpretability and explainability framework
J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.
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AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development
J. Maalouly, D. Hemker, C. Hedayat, C. Rückert, I. Kaufmann, M. Olbrich, S. Lange, H. Mathis, in: 2022 Kleinheubach Conference, IEEE, Miltenberg, Germany, 2022.
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Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods
T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, H. Kuhn, in: 2022 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2022.
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Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.
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Process-Oriented Stream Classification Pipeline: A Literature Review
L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 9094.
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Systematizing the lexicon of platforms in information systems: a data-driven study
C. Bartelheimer, P. zur Heiden, H. Lüttenberg, D. Beverungen, Electronic Markets 32 (2022) 375–396.
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Developing and Evaluating the Concept Data Awareness for K12 Computing Education
L. Höper, in: 21st Koli Calling International Conference on Computing Education Research, Association for Computing Machinery, New York, NY, USA, 2021.
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AutoML for Multi-Label Classification: Overview and Empirical Evaluation
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.
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Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction
T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B. Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain , 2021.
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Conventional and Machine Learning Approaches for Network and Service Coordination
S.B. Schneider, Conventional and Machine Learning Approaches for Network and Service Coordination, 2021.
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Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material
M. Schlichtig, S.A. Opel, L. Budde, C. Schulte, in: E. Jasut, S. Pozdniakov (Eds.), ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings, 2019, pp. 65–73.
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OpenML: An R Package to Connect to the Machine Learning Platform OpenML
G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
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ML-Plan for Unlimited-Length Machine Learning Pipelines
M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.
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Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure
Y. Kim, S. Lee, M. Dollmann, M. Geierhos, International Journal of Advanced Science and Technology 112 (2018) 123–136.
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Leveraging TSP Solver Complementarity through Machine Learning
P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620.
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Semantic Annotation of Software Requirements with Language Frame
Y.-S. Kim, S.-W. Lee, M. Dollmann, M. Geierhos, International Journal of Software Engineering for Smart Device 4 (2017) 1–6.
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Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning
B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320.
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