Generalized Matrix Learning Vector Quantization Computational Method for Intelligent Decision Making: A Systematic Literature Review.
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| Title: | Generalized Matrix Learning Vector Quantization Computational Method for Intelligent Decision Making: A Systematic Literature Review. |
|---|---|
| Authors: | Mumali, Fredrick1 (AUTHOR) fredrick.mumali@put.poznan.pl, Kałkowska, Joanna1 (AUTHOR) Joanna.kalkowska@put.poznan.pl |
| Source: | Archives of Computational Methods in Engineering. Aug2025, Vol. 32 Issue 6, p3885-3907. 23p. |
| Subjects: | Vector quantization, Machine learning, Classification algorithms, Pattern perception, Optimization algorithms |
| Abstract: | Data's increasing complexity and uncertainty across domains continue to drive the demand for more robust, efficient, and accurate computational methods, including machine learning algorithms for pattern recognition and classification problems. Kohonen's Learning Vector Quantization algorithms have been integral to classification algorithms for decades. However, variants such as the Generalized Matrix Learning Vector Quantization have emerged as highly promising and capable computational models for analyzing complex patterns in high-dimensional and noisy datasets with increased performance in recent literature. As a result, this systematic literature review attempts to comprehensively examine recent studies on Generalized Matrix Learning Vector Quantization algorithms, focusing on algorithmic enhancements and variations, inherent features like feature relevance and metric learning, application domains, and performance. Using the Denyer and Tranfield 5-stage systematic literature review method, 61 studies published between 2015 and 2024 are selected for analysis from Scopus, Web of Science, IEEE, and Sprinter. The findings reveal significant advancements and applications of the Generalized Matrix Learning Vector Quantization across healthcare, bioinformatics, and agriculture. The analyzed empirical studies highlight the algorithm's adaptability to various classification problems and enhanced performance. While the cross-disciplinary potential for Generalized Matrix Learning Vector Quantization is well documented, the review identifies gaps in the literature, particularly in the manufacturing domain. Given the rapid advances in manufacturing and the voluminous amounts of data generated, Generalized Matrix Learning Vector Quantization holds great potential in advancing intelligent decision-making across the domain, such as in the selection and management of manufacturing processes. [ABSTRACT FROM AUTHOR] |
| Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186910601 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Generalized Matrix Learning Vector Quantization Computational Method for Intelligent Decision Making: A Systematic Literature Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mumali%2C+Fredrick%22">Mumali, Fredrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fredrick.mumali@put.poznan.pl</i><br /><searchLink fieldCode="AR" term="%22Kałkowska%2C+Joanna%22">Kałkowska, Joanna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Joanna.kalkowska@put.poznan.pl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Archives+of+Computational+Methods+in+Engineering%22">Archives of Computational Methods in Engineering</searchLink>. Aug2025, Vol. 32 Issue 6, p3885-3907. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Data's increasing complexity and uncertainty across domains continue to drive the demand for more robust, efficient, and accurate computational methods, including machine learning algorithms for pattern recognition and classification problems. Kohonen's Learning Vector Quantization algorithms have been integral to classification algorithms for decades. However, variants such as the Generalized Matrix Learning Vector Quantization have emerged as highly promising and capable computational models for analyzing complex patterns in high-dimensional and noisy datasets with increased performance in recent literature. As a result, this systematic literature review attempts to comprehensively examine recent studies on Generalized Matrix Learning Vector Quantization algorithms, focusing on algorithmic enhancements and variations, inherent features like feature relevance and metric learning, application domains, and performance. Using the Denyer and Tranfield 5-stage systematic literature review method, 61 studies published between 2015 and 2024 are selected for analysis from Scopus, Web of Science, IEEE, and Sprinter. The findings reveal significant advancements and applications of the Generalized Matrix Learning Vector Quantization across healthcare, bioinformatics, and agriculture. The analyzed empirical studies highlight the algorithm's adaptability to various classification problems and enhanced performance. While the cross-disciplinary potential for Generalized Matrix Learning Vector Quantization is well documented, the review identifies gaps in the literature, particularly in the manufacturing domain. Given the rapid advances in manufacturing and the voluminous amounts of data generated, Generalized Matrix Learning Vector Quantization holds great potential in advancing intelligent decision-making across the domain, such as in the selection and management of manufacturing processes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=186910601 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11831-025-10267-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 3885 Subjects: – SubjectFull: Vector quantization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Pattern perception Type: general – SubjectFull: Optimization algorithms Type: general Titles: – TitleFull: Generalized Matrix Learning Vector Quantization Computational Method for Intelligent Decision Making: A Systematic Literature Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mumali, Fredrick – PersonEntity: Name: NameFull: Kałkowska, Joanna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 11343060 Numbering: – Type: volume Value: 32 – Type: issue Value: 6 Titles: – TitleFull: Archives of Computational Methods in Engineering Type: main |
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