The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.
Benchmark and Survey of Automated Machine Learning Frameworks
Open accessAbstract
Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.
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Cited by
Machine learning in agriculture domain: A state-of-art survey
Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100010 · Cited by 367
Yuan, H., Yu, K., Xie, F., Liu, M., & Sun, S. (2024) · Medicine Advances · doi:10.1002/med4.75 · Cited by 61
Metadata
- Authors
- Zöller, M.-A., & Huber, M. F.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2021-01-27
- Volume
- 70
- Pages
- 409-472
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2999356315
- Cited by
- 448
- References
- 170
- FWCI
- 26.29
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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