Feature Selection via Chaotic Antlion Optimization
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 032-120577-15394
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0150652
- Title of journal
- Plos One
- Article number
- e0150652
- First page
- -
- Volume
- 11
- Issue
- 3
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0150652&type=printable
- 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
-
2
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 67
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work has been further extended with other bio-inspired algorithm for feature selection and has led to interdisciplinary collaboration and two successful applications in pharmaceutical design: (i) in the modelling of pharmaceutical tabletting processes (Zawbaa, et al. (2018), https://doi.org/10.1016/j.apt.2018.11.008) and (ii) in the modelling of drug release from PLGA microspheres (Zawbaa, et al. (2016), https://doi.org/10.1371/journal.pone.0157610). It has been included in various survey papers: Liu & Wang (2019), https://doi.org/10.1109/ICNSC.2019.8743245, Bhattacharyya et al. (2020), https://doi.org/10.1016/C2018-0-03259-4 and Sharma & Kaur (2020) https://doi.org/10.1007/s11831-020-09412-6.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
