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An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techni...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036546/ https://www.ncbi.nlm.nih.gov/pubmed/36959245 http://dx.doi.org/10.1038/s41598-023-32027-3 |
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author | Vincent, Amala Mary Jidesh, P. |
author_facet | Vincent, Amala Mary Jidesh, P. |
author_sort | Vincent, Amala Mary |
collection | PubMed |
description | For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. |
format | Online Article Text |
id | pubmed-10036546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100365462023-03-25 An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms Vincent, Amala Mary Jidesh, P. Sci Rep Article For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. Nature Publishing Group UK 2023-03-23 /pmc/articles/PMC10036546/ /pubmed/36959245 http://dx.doi.org/10.1038/s41598-023-32027-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vincent, Amala Mary Jidesh, P. An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title | An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title_full | An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title_fullStr | An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title_full_unstemmed | An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title_short | An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms |
title_sort | improved hyperparameter optimization framework for automl systems using evolutionary algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036546/ https://www.ncbi.nlm.nih.gov/pubmed/36959245 http://dx.doi.org/10.1038/s41598-023-32027-3 |
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