BHO-MA: Bayesian hyperparameter optimization with multi-objective acquisition

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dc.contributor.authorDogan, Vedaten
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-01-23T16:31:03Z
dc.date.available2024-01-23T16:31:03Z
dc.date.issued2023-09en
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., 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., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. 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.endpage408en
dc.identifier.isbn978-3-031-53024-1en
dc.identifier.isbn978-3-031-53025-8en
dc.identifier.journaltitleCommunications in Computer and Information Scienceen
dc.identifier.startpage391
dc.identifier.urihttps://hdl.handle.net/10468/15422
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofThird International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part Ien
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.rights© the authors 2024. This is a post-peer-review, pre-copyedit version of a paper published as: Dogan, V., 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., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-53025-8_27en
dc.subjectBayesian optimizationen
dc.subjectMulti-objective optimizationen
dc.subjectHyperparameter tuningen
dc.titleBHO-MA: Bayesian hyperparameter optimization with multi-objective acquisitionen
dc.typeArticle (peer-reviewed)en
dc.typeConference itemen
dc.typebook-chapter
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