Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.
Ensemble algorithm
Ensemble weighted average (EWA)
Radial basis function
Support vector machine
Wheat genotypes classification
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 Sep 2024
30 Sep 2024
Historique:
received:
28
05
2024
accepted:
03
09
2024
medline:
1
10
2024
pubmed:
1
10
2024
entrez:
30
9
2024
Statut:
epublish
Résumé
This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.
Identifiants
pubmed: 39349934
doi: 10.1038/s41598-024-72056-0
pii: 10.1038/s41598-024-72056-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22728Subventions
Organisme : King Khalid University
ID : RGP.2/67/45
Organisme : Princess Nourah Bint Abdulrahman University
ID : PNURSP2024R584
Organisme : National Research Foundation of Korea
ID : NRF-2021R1F1A1062849
Informations de copyright
© 2024. The Author(s).
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