Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach Article Swipe
Lakshmi Priya Swaminatha Rao
,
Suresh Jaganathan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v13.i1.pp775-785
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v13.i1.pp775-785
Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.
Related Topics
Concepts
Hyperparameter
Computer science
Hyperparameter optimization
Bayesian optimization
Benchmark (surveying)
Machine learning
Artificial intelligence
Exploit
Bayesian probability
Adaptive sampling
Algorithm
Monte Carlo method
Mathematics
Support vector machine
Geodesy
Computer security
Statistics
Geography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijai.v13.i1.pp775-785
- https://ijai.iaescore.com/index.php/IJAI/article/download/24324/13881
- OA Status
- diamond
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390177021
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390177021Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.11591/ijai.v13.i1.pp775-785Digital Object Identifier
- Title
-
Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approachWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-25Full publication date if available
- Authors
-
Lakshmi Priya Swaminatha Rao, Suresh JaganathanList of authors in order
- Landing page
-
https://doi.org/10.11591/ijai.v13.i1.pp775-785Publisher landing page
- PDF URL
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https://ijai.iaescore.com/index.php/IJAI/article/download/24324/13881Direct link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://ijai.iaescore.com/index.php/IJAI/article/download/24324/13881Direct OA link when available
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Hyperparameter, Computer science, Hyperparameter optimization, Bayesian optimization, Benchmark (surveying), Machine learning, Artificial intelligence, Exploit, Bayesian probability, Adaptive sampling, Algorithm, Monte Carlo method, Mathematics, Support vector machine, Geodesy, Computer security, Statistics, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approach | 87 |
| abstract_inverted_index.assessed | 117 |
| abstract_inverted_index.building | 7 |
| abstract_inverted_index.compared | 150 |
| abstract_inverted_index.datasets | 122 |
| abstract_inverted_index.identify | 89 |
| abstract_inverted_index.learning | 10, 14, 126 |
| abstract_inverted_index.proposed | 45, 114, 129 |
| abstract_inverted_index.sampling | 86 |
| abstract_inverted_index.tradeoff | 60 |
| abstract_inverted_index.updating | 107 |
| abstract_inverted_index.(Adaptive | 80 |
| abstract_inverted_index.achieving | 56 |
| abstract_inverted_index.algorithm | 30, 74, 115, 132 |
| abstract_inverted_index.benchmark | 119 |
| abstract_inverted_index.different | 145 |
| abstract_inverted_index.hyperband | 79 |
| abstract_inverted_index.threshold | 109 |
| abstract_inverted_index.algorithms | 42 |
| abstract_inverted_index.contextual | 78 |
| abstract_inverted_index.iteration. | 112 |
| abstract_inverted_index.validation | 103 |
| abstract_inverted_index.dynamically | 106 |
| abstract_inverted_index.improvement | 54, 136 |
| abstract_inverted_index.pre-defined | 33 |
| abstract_inverted_index.significant | 4, 135 |
| abstract_inverted_index.traditional | 124 |
| abstract_inverted_index.incorporates | 83 |
| abstract_inverted_index.computational | 142 |
| abstract_inverted_index.configurations | 99 |
| abstract_inverted_index.hyperparameter | 24, 72 |
| abstract_inverted_index.configurations. | 39 |
| abstract_inverted_index.hyperparameters | 38, 148 |
| abstract_inverted_index.state-of-the-art | 152 |
| abstract_inverted_index.algorithms.</p> | 154 |
| abstract_inverted_index.<p>Hyperparameter | 0 |
| abstract_inverted_index.exploration-exploitation | 59 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.75565204 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |