Academic Journal
Balanced Index-Encoding Genetic Algorithm for Extreme Prototype Reduction in k-Nearest Neighbor Classification.
| Τίτλος: | Balanced Index-Encoding Genetic Algorithm for Extreme Prototype Reduction in k-Nearest Neighbor Classification. |
|---|---|
| Συγγραφείς: | Ayala-Ramirez, Victor, Aguilera-Gonzalez, Jose-Gabriel, Tierrasnegras-Badillo, Antonio, Calderon-Uribe, Uriel |
| Πηγή: | Algorithms; Mar2026, Vol. 19 Issue 3, p188, 24p |
| Θεματικοί όροι: | Genetic algorithms, K-nearest neighbor classification, Machine learning, Data reduction, Evolutionary algorithms, Statistical accuracy, Subset selection |
| Περίληψη: | Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ( σ = 0.5 and σ = 1.0 ) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with k ∈ { 1 , 3 , 5 , 7 } using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Algorithms is the property of MDPI 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Balanced Index-Encoding Genetic Algorithm for Extreme Prototype Reduction in k-Nearest Neighbor Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ayala-Ramirez%2C+Victor%22">Ayala-Ramirez, Victor</searchLink><br /><searchLink fieldCode="AR" term="%22Aguilera-Gonzalez%2C+Jose-Gabriel%22">Aguilera-Gonzalez, Jose-Gabriel</searchLink><br /><searchLink fieldCode="AR" term="%22Tierrasnegras-Badillo%2C+Antonio%22">Tierrasnegras-Badillo, Antonio</searchLink><br /><searchLink fieldCode="AR" term="%22Calderon-Uribe%2C+Uriel%22">Calderon-Uribe, Uriel</searchLink> – Name: TitleSource Label: Source Group: Src Data: Algorithms; Mar2026, Vol. 19 Issue 3, p188, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+reduction%22">Data reduction</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Subset+selection%22">Subset selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ( σ = 0.5 and σ = 1.0 ) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with k ∈ { 1 , 3 , 5 , 7 } using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Algorithms is the property of MDPI 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/a19030188 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 188 Subjects: – SubjectFull: Genetic algorithms Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Data reduction Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: Subset selection Type: general Titles: – TitleFull: Balanced Index-Encoding Genetic Algorithm for Extreme Prototype Reduction in k-Nearest Neighbor Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ayala-Ramirez, Victor – PersonEntity: Name: NameFull: Aguilera-Gonzalez, Jose-Gabriel – PersonEntity: Name: NameFull: Tierrasnegras-Badillo, Antonio – PersonEntity: Name: NameFull: Calderon-Uribe, Uriel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19994893 Numbering: – Type: volume Value: 19 – Type: issue Value: 3 Titles: – TitleFull: Algorithms Type: main |
| ResultId | 1 |