Neural Learning from Unbalanced Data
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ANNs and algorithms of the decision tree class, including random forests, can well classify and predict asymmetric data or imbalanced data showing a strong generalization ability.
“Fortunately, ANNs and algorithms of the decision tree class, including random forests, can well classify and predict asymmetric data or imbalanced data showing a strong generalization ability [110,111,112].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [111]
Cites
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“According to [ 14 ], if covariance matrix � is positive definite and symmetric, there exists a unique lower triangular matrix C such that � = CC T . The random vector Z can be represented as:”
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Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
- Authors
- Murphey, Y. L., Guo, H., & Feldkamp, L. A.
- Affiliations
- University of Michigan–Dearborn (US)
- Ford Motor Company (United States) (US)
- Corresponding author
- Murphey, Yi L.
- Venue
- Applied Intelligence
- Published
- 2004-09
- Volume
- 21
- Issue
- 2
- Pages
- 117-128
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W2013262041
- Cited by
- 82
- References
- 20
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 7 · 2021: 4 · 2020: 8 · 2019: 2 · 2018: 4 · 2017: 5 · 2016: 6 · 2015: 3 · 2014: 3 · 2013: 4 · 2012: 1
- FWCI
- 3.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Applied Intelligence (publishedVersion)
- Funders
- National Science Foundation
- Ford Motor Company
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- class imbalance, noisy data, neural network classification, minority class, oversampling, snowball technique, generalization, BP neural network, radial basis function neural network, fuzzy ARTMAP, noise level measurement, vehicle classification, automotive assembly plant
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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