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http://hdl.handle.net/1942/39347
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DC Field | Value | Language |
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dc.contributor.author | MORALES HERNANDEZ, Alejandro | - |
dc.contributor.author | VAN NIEUWENHUYSE, Inneke | - |
dc.contributor.author | ROJAS GONZALEZ, Sebastian | - |
dc.date.accessioned | 2023-01-30T08:20:06Z | - |
dc.date.available | 2023-01-30T08:20:06Z | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-01-27T15:06:22Z | - |
dc.identifier.citation | ARTIFICIAL INTELLIGENCE REVIEW, 56 (8) , p. 8043-8093 | - |
dc.identifier.issn | 0269-2821 | - |
dc.identifier.uri | http://hdl.handle.net/1942/39347 | - |
dc.description.abstract | Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions. | - |
dc.description.sponsorship | This work was supported by the Flanders Artifcial Intelligence Research Program (FLAIR), and by the Research Foundation Flanders (FWO Grant 1216021N). The authors would like to thank Gonzalo Nápoles from Tilburg University for his comments on a previous version of this paper. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.rights | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | - |
dc.subject.other | Hyperparameter optimization | - |
dc.subject.other | Multi-objective optimization | - |
dc.subject.other | Metamodel | - |
dc.subject.other | Meta-heuristic | - |
dc.subject.other | Machine learning | - |
dc.title | A survey on multi-objective hyperparameter optimization algorithms for machine learning | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 8093 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 8043 | - |
dc.identifier.volume | 56 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Morales-Hernandez, A (corresponding author), Hasselt Univ, Fac Sci, Hasselt, Belgium.; Morales-Hernandez, A (corresponding author), Hasselt Univ, VCCM Core Lab & Data Sci Inst, Hasselt, Belgium. | - |
dc.description.notes | alejandro.moraleshernandez@uhasselt.be; | - |
dc.description.notes | inneke.vannieuwenhuyse@uhasselt.be; sebastian.rojasgonzalez@uhasselt.be | - |
local.publisher.place | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1007/s10462-022-10359-2 | - |
dc.identifier.isi | 000903579100001 | - |
dc.identifier.eissn | 1573-7462 | - |
dc.identifier.eissn | 1573-7462 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Morales-Hernandez, Alejandro; Van Nieuwenhuyse, Inneke; Gonzalez, Sebastian Rojas] Hasselt Univ, Fac Sci, Hasselt, Belgium. | - |
local.description.affiliation | [Morales-Hernandez, Alejandro; Van Nieuwenhuyse, Inneke; Gonzalez, Sebastian Rojas] Hasselt Univ, VCCM Core Lab & Data Sci Inst, Hasselt, Belgium. | - |
local.description.affiliation | [Gonzalez, Sebastian Rojas] Univ Ghent, Surrogate Modeling Lab, Ghent, Belgium. | - |
local.uhasselt.international | no | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.contributor | MORALES HERNANDEZ, Alejandro | - |
item.contributor | VAN NIEUWENHUYSE, Inneke | - |
item.contributor | ROJAS GONZALEZ, Sebastian | - |
item.fullcitation | MORALES HERNANDEZ, Alejandro; VAN NIEUWENHUYSE, Inneke & ROJAS GONZALEZ, Sebastian (2023) A survey on multi-objective hyperparameter optimization algorithms for machine learning. In: ARTIFICIAL INTELLIGENCE REVIEW, 56 (8) , p. 8043-8093. | - |
crisitem.journal.issn | 0269-2821 | - |
crisitem.journal.eissn | 1573-7462 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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A survey on multi-objective hyperparameter optimization algorithms for machine learning.pdf | Published version | 3.24 MB | Adobe PDF | View/Open |
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