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DC Field | Value | Language |
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dc.contributor.author | Markatos, NG | - |
dc.contributor.author | Mousavi, A | - |
dc.contributor.author | Katsou, E | - |
dc.contributor.author | Pippione, G | - |
dc.contributor.author | Paoletti, R | - |
dc.date.accessioned | 2024-10-03T16:22:18Z | - |
dc.date.available | 2024-10-03T16:22:18Z | - |
dc.date.issued | 2024-06-21 | - |
dc.identifier | ORCiD: Nikolaos K. Markatos https://orcid.org/0000-0003-3953-6796 | - |
dc.identifier | ORCiD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712 | - |
dc.identifier | ORCiD: Evina Katsou https://orcid.org/0000-0002-2638-7579 | - |
dc.identifier.citation | Markatos, N.K. et al. (2024) 'Feature Selection to address High-Dimensionality in Industry 4.0 Multi-emitter Laser Modules Assembly Lines', IEEE Intelligent Systems, 0 (early access), pp. 1 - 12. doi: 10.1109/MIS.2024.3416678. | en_US |
dc.identifier.issn | 1541-1672 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29871 | - |
dc.description.abstract | Industry 4.0 has increased data depth and breadth in high-tech manufacturing, but high-dimensionality and sparsity persist. High-dimensional space's sparsity makes classical learning and knowledge extraction algorithms ineffective and error-prone. Dimension reduction methods like feature selection seem to address this problem. This study addresses these challenges by conducting a comparative analysis on a real laser assembly industrial case of high dimensions. We explore five standalone methods—NCFS, RReliefF, MRMR, RFE, and Lasso—applied to datasets from two laser modules (d-serie and s-serie). Additionally, two hybrid methods—RReliefF-RFE and MRMR-RFE—are evaluated, broadening the scope of feature selection strategies. Time efficiency prioritizes RReliefF, NCFS and Lasso, while RReliefF-RFE, NCFS and Lasso excel in interpretability, achieving significant predictor reduction without compromising accuracy. The study thus provides insights into the selection of FS methods in a challenging industrial laser assembly setting. | en_US |
dc.description.sponsorship | This work has been carried out in the framework of the IQONIC project, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820677. | en_US |
dc.format.extent | 1 - 12 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Attribution 4.0 International | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | feature extraction | en_US |
dc.subject | redundancy | en_US |
dc.subject | assembly | en_US |
dc.subject | accuracy | en_US |
dc.subject | vectors | en_US |
dc.subject | predictive models | en_US |
dc.subject | laser modes | en_US |
dc.title | Feature Selection to address High-Dimensionality in Industry 4.0 Multi-emitter Laser Modules Assembly Lines | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/MIS.2024.3416678 | - |
dc.relation.isPartOf | IEEE Intelligent Systems | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1941-1294 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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FullText.pdf | Copyright © 2024 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | 882 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License