Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 053-185003-15394
- Type
- D - Journal article
- DOI
-
10.1016/j.swevo.2018.02.021
- Title of journal
- Swarm And Evolutionary Computation
- Article number
- -
- First page
- 29
- Volume
- 42
- Issue
- -
- ISSN
- 2210-6502
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- URL
-
https://www.sciencedirect.com/science/article/pii/S2210650216303042
- Supplementary information
-
-
- 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
-
3
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 36
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a leading journal ranked in top 10% in Computer Science: Artificial Intelligence, the paper addresses feature selection challenges arising from a very large number of attributes combined with a very small number of instances. The proposed novel method has the advantage of providing good learning from fewer examples and fair selection of features from a really large set, all these achieved while ensuring a high standard classification accuracy. Results have been mentioned in other studies, e.g. https://doi.org/10.1007/s00521-019-04477-2, 10.1007/s12065-020-00441-5, 10.1109/ACCESS.2019.2906757.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
