A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

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dc.authoridTokgoz, Selcuk/0009-0002-1480-5099
dc.authoridAcikkar, Mustafa/0000-0001-8888-4987
dc.contributor.authorAcikkar, Mustafa
dc.contributor.authorAltunkol, Yunus
dc.date.accessioned2025-01-06T17:44:33Z
dc.date.available2025-01-06T17:44:33Z
dc.date.issued2023
dc.description.abstractHyperparameter optimization is vital in improving the prediction accuracy of support vector regression (SVR), as in all machine learning algorithms. This study introduces a new hybrid optimization algorithm, namely PSOGS, which consolidates two strong and widely used algorithms, particle swarm optimization (PSO) and grid search (GS). This hybrid algorithm was experimented on five benchmark datasets. The speed and the prediction accuracy of PSOGS-optimized SVR models (PSOGS-SVR) were compared to those of its constituent algorithms (PSO and GS) and another hybrid optimization algorithm (PSOGSA) that combines PSO and gravitational search algorithm (GSA). The prediction accuracies were evaluated and compared in terms of root mean square error and mean absolute percentage error. For the sake of reliability, the results of the experiments were obtained by performing 10-fold cross-validation on 30 runs. The results showed that PSOGS-SVR yields prediction accuracy comparable to GS-SVR, performs much faster than GS-SVR, and provides better results with less execution time than PSO-SVR. Besides, PSOGS-SVR presents more effective results than PSOGSA-SVR in terms of both prediction accuracy and execution time. As a result, this study proved that PSOGS is a fast, stable, efficient, and reliable algorithm for optimizing hyperparameters of SVR.
dc.identifier.doi10.1007/s00521-023-08805-5
dc.identifier.endpage19977
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue27
dc.identifier.scopus2-s2.0-85164828979
dc.identifier.scopusqualityQ1
dc.identifier.startpage19961
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08805-5
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3086
dc.identifier.volume35
dc.identifier.wosWOS:001028533400003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectSupport vector regression
dc.subjectHyperparameter optimization
dc.subjectGrid search
dc.subjectParticle swarm optimization
dc.titleA novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression
dc.typeArticle

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