A multiclass classification using one-versus-all approach with the differential partition sampling ensemble
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Recent artificial intelligence developments for multiclass classification using ML are well suited to combine different connectivity modalities and methods to test the power of multiclass classification.
“Recent artificial intelligence developments for multiclass classification (Churcher et al., 2021; Gao et al., 2021; Hou et al., 2021) using ML are well suited to combine different connectivity modalities and methods to test the power of multiclass classification.”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Gao et al., 2021
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References 58 · Cited by 44
- Authors
- Gao, X., He, Y., Zhang, M., Diao, X., Jing, X., Ren, B., & Ji, W.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- China Electric Power Research Institute (CN)
- Corresponding author
- Gao, Xin
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 97
- Pages
- 104034
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W3096363578
- Cited by
- 44
- References
- 58
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 11 · 2023: 3 · 2022: 12 · 2021: 1 · 2020: 1
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- multi-class classification, class imbalance, random undersampling, SMOTE, oversampling, undersampling, ensemble learning, neighborhood information
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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