Feature Selection to Address High Dimensionality in Industry 4.0 Multi-Emitter Laser Modules Assembly Lines Article Swipe
Nikolaos Grigorios Markatos
,
Alireza Mousavi
,
Evina Katsou
,
Giulia Pippione
,
Roberto Paoletti
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/mis.2024.3416678
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/mis.2024.3416678
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.
Related Topics
Concepts
Computer science
Curse of dimensionality
Feature selection
Common emitter
Selection (genetic algorithm)
Feature (linguistics)
Artificial intelligence
Feature extraction
Dimensionality reduction
Pattern recognition (psychology)
Engineering drawing
Electrical engineering
Engineering
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/mis.2024.3416678
- OA Status
- hybrid
- Cited By
- 1
- References
- 19
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399881429Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/mis.2024.3416678Digital Object Identifier
- Title
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Feature Selection to Address High Dimensionality in Industry 4.0 Multi-Emitter Laser Modules Assembly LinesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-21Full publication date if available
- Authors
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Nikolaos Grigorios Markatos, Alireza Mousavi, Evina Katsou, Giulia Pippione, Roberto PaolettiList of authors in order
- Landing page
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https://doi.org/10.1109/mis.2024.3416678Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1109/mis.2024.3416678Direct OA link when available
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Computer science, Curse of dimensionality, Feature selection, Common emitter, Selection (genetic algorithm), Feature (linguistics), Artificial intelligence, Feature extraction, Dimensionality reduction, Pattern recognition (psychology), Engineering drawing, Electrical engineering, Engineering, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data | 4 |
| abstract_inverted_index.five | 62 |
| abstract_inverted_index.from | 72 |
| abstract_inverted_index.high | 58 |
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| abstract_inverted_index.seem | 35 |
| abstract_inverted_index.this | 38 |
| abstract_inverted_index.thus | 117 |
| abstract_inverted_index.Lasso | 104 |
| abstract_inverted_index.MRMR, | 66 |
| abstract_inverted_index.depth | 5 |
| abstract_inverted_index.excel | 105 |
| abstract_inverted_index.laser | 53, 74, 130 |
| abstract_inverted_index.makes | 19 |
| abstract_inverted_index.scope | 88 |
| abstract_inverted_index.study | 41, 116 |
| abstract_inverted_index.these | 43 |
| abstract_inverted_index.while | 100 |
| abstract_inverted_index.Lasso, | 99 |
| abstract_inverted_index.hybrid | 81 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.breadth | 7 |
| abstract_inverted_index.explore | 61 |
| abstract_inverted_index.feature | 33, 90 |
| abstract_inverted_index.methods | 31, 125 |
| abstract_inverted_index.modules | 75 |
| abstract_inverted_index.space's | 17 |
| abstract_inverted_index.without | 112 |
| abstract_inverted_index.(d-serie | 76 |
| abstract_inverted_index.Industry | 0 |
| abstract_inverted_index.analysis | 49 |
| abstract_inverted_index.assembly | 54, 131 |
| abstract_inverted_index.datasets | 71 |
| abstract_inverted_index.insights | 119 |
| abstract_inverted_index.learning | 21 |
| abstract_inverted_index.persist. | 15 |
| abstract_inverted_index.problem. | 39 |
| abstract_inverted_index.provides | 118 |
| abstract_inverted_index.setting. | 132 |
| abstract_inverted_index.sparsity | 14, 18 |
| abstract_inverted_index.Dimension | 29 |
| abstract_inverted_index.RReliefF, | 65, 96 |
| abstract_inverted_index.accuracy. | 114 |
| abstract_inverted_index.achieving | 108 |
| abstract_inverted_index.addresses | 42 |
| abstract_inverted_index.classical | 20 |
| abstract_inverted_index.high-tech | 9 |
| abstract_inverted_index.increased | 3 |
| abstract_inverted_index.knowledge | 23 |
| abstract_inverted_index.predictor | 110 |
| abstract_inverted_index.reduction | 30, 111 |
| abstract_inverted_index.s-serie). | 78 |
| abstract_inverted_index.selection | 34, 91, 122 |
| abstract_inverted_index.algorithms | 25 |
| abstract_inverted_index.broadening | 86 |
| abstract_inverted_index.challenges | 44 |
| abstract_inverted_index.conducting | 46 |
| abstract_inverted_index.efficiency | 94 |
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| abstract_inverted_index.dimensions. | 59 |
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| abstract_inverted_index.strategies. | 92 |
| abstract_inverted_index.compromising | 113 |
| abstract_inverted_index.error-prone. | 28 |
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| abstract_inverted_index.RReliefF-RFE, | 101 |
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| abstract_inverted_index.manufacturing, | 10 |
| abstract_inverted_index.Lasso—applied | 69 |
| abstract_inverted_index.methods—NCFS, | 64 |
| abstract_inverted_index.High-dimensional | 16 |
| abstract_inverted_index.interpretability, | 107 |
| abstract_inverted_index.high-dimensionality | 12 |
| abstract_inverted_index.methods—RReliefF-RFE | 82 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.65774484 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |