Enriched ant colony optimization and its application in feature selection
- Submitting institution
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Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6724192
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2014.03.053
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 354
- Volume
- 142
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
-
- 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
-
4
- Research group(s)
-
-
- Citation count
- 35
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This novel work has contributed to motivate the research for graph-based ACO for feature selection (Moradu et al., 2015), a rough set-based harmony search feature selection technique (Inbarani et al., 2015), image-based content retrieval using ACO feature selection (Rashno et al., 2015), and multiple parameter control for ACO feature selection (Wang et al., 2015), amongst many others. Neurocomputing is a leading outlet in the subject area.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -