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http://bura.brunel.ac.uk/handle/2438/29871| Title: | Feature Selection to address High-Dimensionality in Industry 4.0 Multi-emitter Laser Modules Assembly Lines |
| Authors: | Markatos, NG Mousavi, A Katsou, E Pippione, G Paoletti, R |
| Keywords: | feature extraction;redundancy;assembly;accuracy;vectors;predictive models;laser modes |
| Issue Date: | 21-Jun-2024 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| 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. |
| 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. |
| URI: | https://bura.brunel.ac.uk/handle/2438/29871 |
| DOI: | https://doi.org/10.1109/MIS.2024.3416678 |
| ISSN: | 1541-1672 |
| Other Identifiers: | ORCiD: Nikolaos K. Markatos https://orcid.org/0000-0003-3953-6796 ORCiD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712 ORCiD: Evina Katsou https://orcid.org/0000-0002-2638-7579 |
| Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 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 |
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