TY  - JOUR
A1  - Mueller, Tobias
A1  - Segin, Alexander
A1  - Weigand, Christoph
A1  - Schmitt, Robert H.
T1  - Feature selection for measurement models
T2  - International journal of quality & reliability management
N2  - 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.
KW  - Feature selection
KW  - Modelling
KW  - Measurement models
KW  - Measurement uncertainty
Y1  - 2022
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/10352
SN  - 0265-671X
IS  - Vol. ahead-of-print, No. ahead-of-print.
PB  - Emerald Group Publishing Limited
CY  - Bingley
ER  -