Tobias Mueller, Alexander Segin, Christoph Weigand, R. H. Schmitt
{"title":"测量模型的特征选择","authors":"Tobias Mueller, Alexander Segin, Christoph Weigand, R. H. Schmitt","doi":"10.1108/ijqrm-07-2021-0245","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":14193,"journal":{"name":"International Journal of Quality & Reliability Management","volume":"26 13","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Feature selection for measurement models\",\"authors\":\"Tobias Mueller, Alexander Segin, Christoph Weigand, R. H. Schmitt\",\"doi\":\"10.1108/ijqrm-07-2021-0245\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":14193,\"journal\":{\"name\":\"International Journal of Quality & Reliability Management\",\"volume\":\"26 13\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Quality & Reliability Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/ijqrm-07-2021-0245\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Quality & Reliability Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijqrm-07-2021-0245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
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.
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