A fine-grained Random Forests using class decomposition: an application to medical diagnosis
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D3001
- Type
- D - Journal article
- DOI
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10.1007/s00521-015-2064-z
- Title of journal
- Neural Computing and Applications
- Article number
- -
- First page
- 2279
- Volume
- 27
- Issue
- 8
- ISSN
- 0941-0643
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
-
- 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)
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-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed method improves the accuracy of Random Forests for classification on a range of medical diagnosis data sets. We argue that the proposed class decomposition applied to Random Forests is likely to have a consistent boost up of accuracy in medical diagnosis, due to the dual role of class decomposition in diversification of the ensemble, and the natural clustering in medical diagnosis data sets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -