{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:25:48Z","timestamp":1777983948837,"version":"3.51.4"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000646","name":"Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research","doi-asserted-by":"publisher","award":["23K11310"],"award-info":[{"award-number":["23K11310"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3539209","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T13:43:00Z","timestamp":1738676580000},"page":"26208-26224","source":"Crossref","is-referenced-by-count":0,"title":["EVI-GPBO: Estimated Variance Integration-Based Gaussian Process Bayesian Optimization"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5924-6959","authenticated-orcid":false,"given":"Yuto","family":"Omae","sequence":"first","affiliation":[{"name":"College of Industrial Technology, Nihon University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0346-3831","authenticated-orcid":false,"given":"Yohei","family":"Kakimoto","sequence":"additional","affiliation":[{"name":"College of Industrial Technology, Nihon University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-1569","authenticated-orcid":false,"given":"Makoto","family":"Sasaki","sequence":"additional","affiliation":[{"name":"College of Industrial Technology, Nihon University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3664-2681","authenticated-orcid":false,"given":"Masaya","family":"Mori","sequence":"additional","affiliation":[{"name":"College of Industrial Technology, Nihon University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Practical Bayesian optimization of machine learning algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Snoek"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s21072411"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103223"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/stc.2693"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/WCI.2015.7495509"},{"key":"ref6","article-title":"An efficient classification framework for breast cancer using hyper parameter tuned random decision forest classifier and Bayesian optimization","volume":"68","author":"Kumar","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref7","first-page":"1","article-title":"Bayesian optimization for a better dessert","volume-title":"Proc. NIPS Workshop Bayesian Optim.","author":"Solnik"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.127270"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-020-02720-2"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23871-5_3"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.11.026"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.md.2016.04.001"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2018.03.001"},{"key":"ref14","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"24","author":"Bergstra"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3569052.3578923"},{"key":"ref16","article-title":"Tree-structured Parzen estimator: Understanding its algorithm components and their roles for better empirical performance","author":"Watanabe","year":"2023","journal-title":"arXiv:2304.11127"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/63.3.413"},{"key":"ref18","first-page":"2623","article-title":"Optuna: A next-generation hyperparameter optimization framework","volume-title":"Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining","author":"Akiba"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103456"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.04.026"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1115\/1.3653121"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/buildings13123010"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s40899-024-01064-9"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.2c05229"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"ref26","first-page":"5381","article-title":"Improving the expected improvement algorithm","volume-title":"NIPS, Proc. 31st Int. Conf. Neural Inf. Process. Syst.","volume":"30","author":"Qin"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10928-8_37"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/e20030201"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/psf2021003003"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICBASE53849.2021.00032"},{"key":"ref31","first-page":"1015","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design","volume-title":"Proc. 27th Int. Conf. Int. Conf. Mach. Learn.","author":"Srinivas"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.1992.271617"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1103\/physrevaccelbeams.27.084801"},{"key":"ref34","volume-title":"Gaussian Process and Machine Learning","author":"Mochihashi","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5954\/ICAROB.2021.OS10-7"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/316"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3555050.3569115"},{"key":"ref38","first-page":"253","article-title":"Gaussian process optimization with mutual information","volume-title":"Proc. 31st Int. Conf. Int. Conf. Mach. Learn.","author":"Contal"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-59100-9"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3390\/genes10080608"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1081\/1\/012023"},{"key":"ref42","first-page":"17708","article-title":"Diversity-guided multi-objective Bayesian optimization with batch evaluations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lukovi\u0107"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.13188"},{"key":"ref44","first-page":"507","article-title":"Multi-objective Bayesian optimization over high-dimensional search spaces","volume-title":"Proc. 38th Conf. Uncertainty Artif. Intell.","author":"Daulton"},{"key":"ref45","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121056","article-title":"Taking another step: A simple approach to high-dimensional Bayesian optimization","volume":"679","author":"Gui","year":"2024","journal-title":"Inf. Sci."},{"key":"ref46","article-title":"Error functions and Fresnel integrals","volume-title":"Handbook of Mathematical Functions With Formulas, Graphs, and Mathematical Tables","volume":"55","author":"Abramowitz","year":"1972"},{"key":"ref47","volume-title":"Scipy.Integrate.dblquad","year":"2025"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-61786-7"},{"key":"ref49","volume-title":"Virtual Library of Simulation Experiments: Test Functions and Datasets","author":"Surjanovic","year":"2013"},{"key":"ref50","first-page":"411","article-title":"A comparative study on machine learning techniques using Titanic dataset","volume-title":"Proc. 7th Int. Conf. Adv. Technol.","author":"Ekinci"},{"key":"ref51","volume-title":"The Boston House-Price Data","year":"2024"},{"key":"ref52","volume-title":"Toy Datasets (7.1.2. Diabetes Dataset)\u2014Scikit-Learn 1.4.Dev0 Documentation","year":"2024"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/Confluence47617.2020.9057955"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/0095-0696(78)90006-2"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CCAA.2017.8229835"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3220267.3220282"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3390\/app9235191"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3390\/stats4040051"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.07.002"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000067.MR2060166"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119549"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2024.3394928"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevAccelBeams.26.034601"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10872956.pdf?arnumber=10872956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T14:07:52Z","timestamp":1739974072000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10872956\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3539209","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.172107347.71797167\/v1","asserted-by":"object"}]},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}