A Genetic Algorithm Approach to Optimising Random Forests Applied to Class Engineered Data
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0028
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2016.08.007
- Title of journal
- Information Science
- Article number
- -
- First page
- 220
- Volume
- 384
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2017
- URL
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https://www.sciencedirect.com/science/article/pii/S0020025516305783
- 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
-
-
- Research group(s)
-
-
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed method has a particular strength in the medical domain, showing an accuracy boost-up in automatic prediction of diseases like diabetes, Parkinsons, and breast cancer.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -