A genetic algorithm approach to optimising random forests applied to class engineered data
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Elyan_4
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2016.08.007
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 220-234
- Volume
- 384
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
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-
- 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
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel diversity ensemble-learning based methods introduced in this paper, were successfully applied to a range of applications and led to a KTP funded project (£136,000 - KTP No. 10709) to mine a large volume of unstructured data (https://rgu-repository.worktribe.com/project/113968/data-mining-and-visualisation-tool-to-intelligently-mine-large-volumes-of-structured-and-unstructured-data-in-oilgas-operations). An early version of this method was also successfully applied to real-world problems via a £100,000 collaborative project grant to process and analyse engineering diagrams (http://dx.doi.org/10.1109/IJCNN.2018.8489087)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
