测量模型的特征选择

Tobias Mueller, Alexander Segin, Christoph Weigand, R. H. Schmitt
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引用次数: 2

摘要

目的在确定测量不确定度时,GUM程序需要建立一个测量模型,在被测量和所有影响量之间建立函数关系。由于建模和量化测量不确定性的努力取决于所考虑的影响量的数量,因此本研究的目的是确定相关影响量,并从数据集中删除不相关的影响量。设计/方法/方法在这项工作中,研究了是否可以通过使用特征选择(FS)方法来减少确定测量不确定度的建模工作量。为此,在16个人工测试数据集上测试了9种不同的FS方法,这些数据集的特性(数据点数量、特征数量、复杂性、低影响特征和冗余特征)通过实验设计而变化。结果基于成功度量、方法的稳定性、通用性和复杂性,可以识别出两种FS方法,它们可以可靠地识别测量模型的相关和不相关影响量。原创性/价值首次将FS方法应用于具有经典测量过程特性的数据集。基于仿真的结果为测量模型在FS领域的进一步研究奠定了基础。所识别的算法将在未来应用于实际测量过程。
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Feature selection for measurement models
PurposeIn 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/approachIn 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.FindingsBased 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/valueFor 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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来源期刊
CiteScore
5.60
自引率
12.00%
发文量
53
期刊介绍: In today''s competitive business and industrial environment, it is essential to have an academic journal offering the most current theoretical knowledge on quality and reliability to ensure that top management is fully conversant with new thinking, techniques and developments in the field. The International Journal of Quality & Reliability Management (IJQRM) deals with all aspects of business improvements and with all aspects of manufacturing and services, from the training of (senior) managers, to innovations in organising and processing to raise standards of product and service quality. It is this unique blend of theoretical knowledge and managerial relevance that makes IJQRM a valuable resource for managers striving for higher standards.Coverage includes: -Reliability, availability & maintenance -Gauging, calibration & measurement -Life cycle costing & sustainability -Reliability Management of Systems -Service Quality -Green Marketing -Product liability -Product testing techniques & systems -Quality function deployment -Reliability & quality education & training -Productivity improvement -Performance improvement -(Regulatory) standards for quality & Quality Awards -Statistical process control -System modelling -Teamwork -Quality data & datamining
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