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Article

Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods

1
Institute of Automation Technology, Helmut-Schmidt-University, 22043 Hamburg, Germany
2
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Sensors 2021, 21(7), 2397; https://doi.org/10.3390/s21072397
Received: 14 March 2021 / Revised: 26 March 2021 / Accepted: 28 March 2021 / Published: 30 March 2021
In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning methods, and their intrinsic hyper-parameters are hugely varied. Since no agreed-on datasets for CPS exist, developers of new algorithms are forced to define their own benchmarks. This leads to a large number of algorithms each claiming benefits over other approaches but lacking a fair comparison. To tackle this problem, this paper defines a novel model for a generation process of data, similar to that found in CPS. The model is based on well-understood system theory and allows many datasets with different characteristics in terms of complexity to be generated. The data will pave the way for a comparison of selected machine learning methods in the exemplary field of unsupervised learning. Based on the synthetic CPS data, the data generation process is evaluated by analyzing the performance of the methods of the Self-Organizing Map, One-Class Support Vector Machine and Long Short-Term Memory Neural Net in anomaly detection. View Full-Text
Keywords: machine learning; artificial data; anomaly detection machine learning; artificial data; anomaly detection
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MDPI and ACS Style

Zimmering, B.; Niggemann, O.; Hasterok, C.; Pfannstiel, E.; Ramming, D.; Pfrommer, J. Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods. Sensors 2021, 21, 2397. https://doi.org/10.3390/s21072397

AMA Style

Zimmering B, Niggemann O, Hasterok C, Pfannstiel E, Ramming D, Pfrommer J. Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods. Sensors. 2021; 21(7):2397. https://doi.org/10.3390/s21072397

Chicago/Turabian Style

Zimmering, Bernd, Oliver Niggemann, Constanze Hasterok, Erik Pfannstiel, Dario Ramming, and Julius Pfrommer. 2021. "Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods" Sensors 21, no. 7: 2397. https://doi.org/10.3390/s21072397

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