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

 
: Zimmering, Bernd; Niggemann, Oliver; Hasterok, Constanze; Pfannstiel, Erik; Ramming, Dario; Pfrommer, Julius

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Volltext urn:nbn:de:0011-n-6336638 (1.3 MByte PDF)
MD5 Fingerprint: 195183410b916e3d986e5a0179cb4ed0
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Erstellt am: 13.4.2021


Sensors. Online journal 21 (2021), Nr.7, Art. 2397, 14 S.
http://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
machine learning; artificial data; anomaly detection

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-633663.html