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

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

In the field of Cyber-Physical Systems (CPS), there is a large number 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 n...

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Bibliographic Details
Main Authors: Bernd Zimmering, Oliver Niggemann, Constanze Hasterok, Erik Pfannstiel, Dario Ramming, Julius Pfrommer
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Sensors
Subjects:
machine learning
artificial data
anomaly detection
Online Access:https://www.mdpi.com/1424-8220/21/7/2397

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