Feature reduction and selection for use in machine learning for manufacturing

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dc.contributor.authorAlrufaihi, Duaa
dc.contributor.authorOleghe, Omogbai
dc.contributor.authorAlmanei, Mohammed
dc.contributor.authorJagtap, Sandeep
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2022-11-16T10:39:43Z
dc.date.available2022-11-16T10:39:43Z
dc.date.issued2022-09-08
dc.description.abstractIn a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several ML algorithms, including feature reduction/selection methods, were applied to a publicly available multistage manufacturing dataset to predict the characteristic of the output measurements in (mm). A total of 24 prediction models were produced. The accuracy of the prediction models and the execution time were the evaluation metrics. The results show that uncontrolled variables are the most common features that have been selected by the selection/reduction methods suggesting their strong relationship to the quality of the product. The performance of the prediction models was heavily dependent on the ML algorithm.en_UK
dc.identifier.citationAlrufaihi D, Oleghe O, Almanei M, et al., (2022) Feature reduction and selection for use in machine learning for manufacturing. In: Advances in Transdisciplinary Engineering, Volume 25: Advances in Manufacturing Technology XXXV. Proceedings of ICMR 2022, 19th International Conference in Manufacturing Research, Incorporating the 36th National Conference in Manufacturing Research, 6-8 September, Derby, UK, pp. 289-296en_UK
dc.identifier.eisbn978-1-64368-331-7
dc.identifier.eissn2352-7528
dc.identifier.isbn978-1-64368-330-0
dc.identifier.issn2352-751X
dc.identifier.urihttps://doi.org/10.3233/ATDE220605
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18714
dc.language.isoenen_UK
dc.publisherIOS Pressen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMachine Learningen_UK
dc.subjectalgorithmsen_UK
dc.subjectComplex manufacturing systemsen_UK
dc.titleFeature reduction and selection for use in machine learning for manufacturingen_UK
dc.typeConference paperen_UK

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