Articles | Volume 12, issue 1
https://doi.org/10.5194/jsss-12-45-2023
https://doi.org/10.5194/jsss-12-45-2023
Regular research article
 | 
27 Jan 2023
Regular research article |  | 27 Jan 2023

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Tanja Dorst, Tizian Schneider, Sascha Eichstädt, and Andreas Schütze

Viewed

Total article views: 829 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
611 195 23 829 25 22
  • HTML: 611
  • PDF: 195
  • XML: 23
  • Total: 829
  • BibTeX: 25
  • EndNote: 22
Views and downloads (calculated since 27 Jan 2023)
Cumulative views and downloads (calculated since 27 Jan 2023)

Viewed (geographical distribution)

Total article views: 798 (including HTML, PDF, and XML) Thereof 798 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 2024
Download
Short summary
A fundamental problem of machine learning (ML) is measurement uncertainty and the influence on ML results. Measurement uncertainty, which is critical in hazardous gas detection, is directly addressed in this paper. A previously published toolbox is extended for regression. One of the benefits of this approach is obtaining a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision.