AutoML: A systematic review on automated machine learning with neural architecture search
AutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine lear...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | Journal of Information and Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715923000604 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850055608424726528 |
|---|---|
| author | Imrus Salehin Md. Shamiul Islam Pritom Saha S.M. Noman Azra Tuni Md. Mehedi Hasan Md. Abu Baten |
| author_facet | Imrus Salehin Md. Shamiul Islam Pritom Saha S.M. Noman Azra Tuni Md. Mehedi Hasan Md. Abu Baten |
| author_sort | Imrus Salehin |
| collection | DOAJ |
| description | AutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied. In particular, research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning. In this semantic review research, we summarize the data processing requirements for AutoML approaches and provide a detailed explanation. We place greater emphasis on neural architecture search (NAS) as it currently represents a highly popular sub-topic within the field of AutoML. NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task. We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10, CIFAR-100, ImageNet and well-known benchmark datasets. Additionally, we delve into several noteworthy research directions in NAS methods including one/two-stage NAS, one-shot NAS and joint hyperparameter with architecture optimization. We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed. To conclude, we examine several open problems (SOTA problems) within current AutoML methods that assure further investigation in future research. |
| format | Article |
| id | doaj-art-9c5bb41461234d7384e44e731de2e541 |
| institution | DOAJ |
| issn | 2949-7159 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-9c5bb41461234d7384e44e731de2e5412025-08-20T02:51:56ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592024-01-0121528110.1016/j.jiixd.2023.10.002AutoML: A systematic review on automated machine learning with neural architecture searchImrus Salehin0Md. Shamiul Islam1Pritom Saha2S.M. Noman3Azra Tuni4Md. Mehedi Hasan5Md. Abu Baten6Department of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea; Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt 60318, GermanyDepartment of Computer Science and Engineering, University of Asia Pacific, Dhaka 1205, BangladeshDepartment of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, BangladeshDepartment of Computer Science and Engineering, Northern University Bangladesh, Dhaka 1215, BangladeshAutoML (Automated Machine Learning) is an emerging field that aims to automate the process of building machine learning models. AutoML emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is applied. In particular, research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and tuning. In this semantic review research, we summarize the data processing requirements for AutoML approaches and provide a detailed explanation. We place greater emphasis on neural architecture search (NAS) as it currently represents a highly popular sub-topic within the field of AutoML. NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task. We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10, CIFAR-100, ImageNet and well-known benchmark datasets. Additionally, we delve into several noteworthy research directions in NAS methods including one/two-stage NAS, one-shot NAS and joint hyperparameter with architecture optimization. We discussed how the search space size and complexity in NAS can vary depending on the specific problem being addressed. To conclude, we examine several open problems (SOTA problems) within current AutoML methods that assure further investigation in future research.http://www.sciencedirect.com/science/article/pii/S2949715923000604AutoMLNeural architecture searchAdvance machine learningSearch spaceHyperparameter optimization |
| spellingShingle | Imrus Salehin Md. Shamiul Islam Pritom Saha S.M. Noman Azra Tuni Md. Mehedi Hasan Md. Abu Baten AutoML: A systematic review on automated machine learning with neural architecture search Journal of Information and Intelligence AutoML Neural architecture search Advance machine learning Search space Hyperparameter optimization |
| title | AutoML: A systematic review on automated machine learning with neural architecture search |
| title_full | AutoML: A systematic review on automated machine learning with neural architecture search |
| title_fullStr | AutoML: A systematic review on automated machine learning with neural architecture search |
| title_full_unstemmed | AutoML: A systematic review on automated machine learning with neural architecture search |
| title_short | AutoML: A systematic review on automated machine learning with neural architecture search |
| title_sort | automl a systematic review on automated machine learning with neural architecture search |
| topic | AutoML Neural architecture search Advance machine learning Search space Hyperparameter optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2949715923000604 |
| work_keys_str_mv | AT imrussalehin automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT mdshamiulislam automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT pritomsaha automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT smnoman automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT azratuni automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT mdmehedihasan automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch AT mdabubaten automlasystematicreviewonautomatedmachinelearningwithneuralarchitecturesearch |