A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty
- Submitting institution
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Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 348-214535-7003633
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2018.08.017
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 58
- Volume
- 470
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Technical exception
- Month of publication
- August
- Year of publication
- 2018
- URL
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http://eprints.whiterose.ac.uk/134706/2/ELSEVI_3.pdf
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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9
- Research group(s)
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1 - Energy & Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Correlation analysis is a traditional statistical technique to determine the dependency between variables for many real-life problems. An increasing number of electrical sensors create big data and the data may have variance error due to uncertainty. This international collaboration developed a robust correlation analysis framework to minimise correlation coefficient deviation under a data imbalance environment. The proposed model was applied to a real-life scenario to examine the dependency of solar resource availability and weather conditions. This work is supported by the National Natural Science Foundation of China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -