Feature selection for measurement models

  • Purpose In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset. Design/methodology/approach In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments. Findings Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model. Originality/value For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

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Metadaten
Author:Tobias Mueller, Alexander Segin, Christoph Weigand, Robert H. Schmitt
DOI:https://doi.org/10.1108/IJQRM-07-2021-0245
ISSN:0265-671X
Parent Title (English):International journal of quality & reliability management
Publisher:Emerald Group Publishing Limited
Place of publication:Bingley
Document Type:Article
Language:English
Year of Completion:2022
Date of first Publication:2022/01/25
Date of the Publication (Server):2022/12/20
Tag:Feature selection; Measurement models; Measurement uncertainty; Modelling
Issue:Vol. ahead-of-print, No. ahead-of-print.
Link:http://dx.doi.org/10.1108/IJQRM-07-2021-0245
Zugriffsart:bezahl
Institutes:FH Aachen / Fachbereich Wirtschaftswissenschaften
collections:Verlag / Emerald Group Publishing Limited