Generating artificial sensor data for the comparison of unsupervised machine learning methods
Publication date
2021-03-30
Document type
Forschungsartikel
Author
Hasterok, Constanze
Pfannstiel, Erik
Pfrommer, Julius
Organisational unit
Publisher
MDPI
Series or journal
Sensors
ISSN
Periodical volume
21
Periodical issue
7
Article ID
2397
Part of the university bibliography
✅
Language
English
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.
Description
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Published version
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