BHO-MA: Bayesian Hyperparameter Optimization with Multi-objective Acquisition

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dc.contributor.authorDogan, Vedaten
dc.contributor.authorPrestwich, Stevenen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-21T13:22:57Z
dc.date.available2025-01-21T13:22:57Z
dc.date.issued2024-02-01en
dc.description.abstractGood hyperparameter values are crucial for the performance of machine learning models. In particular, poorly chosen values can cause under- or overfitting in regression and classification. A common approach to hyperparameter tuning is grid search, but this is crude and computationally expensive, and the literature contains several more efficient automatic methods such as Bayesian optimization. In this work, we develop a Bayesian hyperparameter optimization technique with more robust performance, by combining several acquisition functions and applying a multi-objective approach. We evaluated our method using both classification and regression tasks. We selected four data sets from the literature and compared the performance with eight popular methods. The results show that the proposed method achieved better results than all others.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDogan, V. and Prestwich, S. (2024) 'BHO-MA: Bayesian Hyperparameter Optimization with Multi-objective Acquisition', in Pereira, A. I., Mendes, A., Fernandes, F. P., Pacheco, M. F., Coelho, J. P. and Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981, pp 391-408. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_27en
dc.identifier.doihttps://doi.org/10.1007/978-3-031-53025-8_27en
dc.identifier.eissn1865-0937en
dc.identifier.endpage408en
dc.identifier.isbn9783031530241en
dc.identifier.isbn9783031530258en
dc.identifier.issn1865-0929en
dc.identifier.journaltitleCommunications in Computer and Information Scienceen
dc.identifier.startpage391en
dc.identifier.urihttps://hdl.handle.net/10468/16861
dc.identifier.volume1981en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.relation.ispartofOptimization, Learning Algorithms and Applicationsen
dc.relation.ispartofOL2A 2023, Ponta Delgada (Portugal) and online, 27-29 September 2023en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectBayesian optimizationen
dc.subjectMulti-objective optimizationen
dc.subjectHyperparameter tuningen
dc.titleBHO-MA: Bayesian Hyperparameter Optimization with Multi-objective Acquisitionen
dc.typeConference itemen
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