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Cited by 29Energy Efficient Wireless Sensor NetworksInternet of Things and AISmart Agriculture and AI

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An intelligent scheme for big data recovery in Internet of Things based on Multi-Attribute assistance and Extremely randomized trees

Cheng, Hongju, Shi, Yushi, Wu, Leihuo, Guo, Yingya, Xiong, Naixue·2021·Information Sciences
DOI 10.1016/j.ins.2020.12.041
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  • Schelter et al. [57] proposed Hedgecut, a classification model based on extremely randomized trees (ERTs).

    “To improve the efficiency of the unlearning process for tree-based machine learning models, Schelter et al. [57] proposed Hedgecut , a classification model based on extremely randomized trees (ERTs) [100].”

    From Machine Unlearning: A Survey (2024) · cites this paper as [100]

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References 47 · Cited by 29

Research works printed but not linked: 44

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Metadata

Authors
Cheng, H., Shi, Y., Wu, L., Guo, Y., & Xiong, N.
Affiliations
  • Fuzhou University (CN)
  • Northeastern State University (US)
Corresponding author
Cheng, Hongju
ORCID
Venue
Information Sciences
Publisher
Elsevier BV
Published
2021-05
Volume
557
Pages
66-83
ISSN
0020-0255 (ISSN-L)
DOI
10.1016/j.ins.2020.12.041
OpenAlex
W3120613103
Cited by
29
References
47
Citations by year
2026: 1 · 2025: 3 · 2024: 3 · 2023: 8 · 2022: 9 · 2021: 5
FWCI
2.80
Type
journal article
Language
en
Open access
closed
Licence
Locations
Information Sciences (publishedVersion)
Funders
  • National Natural Science Foundation of China
  • Natural Science Foundation of Fujian Province
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
missing data recovery, Internet of Things, extra trees, outlier detection, data denoising, Spearman correlation, sensor data, data loss

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