A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection
- Submitting institution
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Sheffield Hallam University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1395
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2014.10.031
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 494
- Volume
- 295
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- 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
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3
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Originality: A novel preference operator for evolutionary multi-objective optimisation is proposed addressing some of the key challenges of many-objective optimisation by allowing the decision maker to influence the direction of search.
Significance: A result of collaboration with the Sensing & Imaging Group at MMU, the proposed method results in significantly better performance on a range of benchmark problems than the state-of-the-art. Significant improvement over existing solutions for real-world concealed weapons detection is shown. Methods are used in several follow-on papers in medical decision making: (https://ieeexplore.ieee.org/abstract/document/8058553 & https://ieeexplore.ieee.org/abstract/document/7300294).
Rigour: Evaluation performed using a range of statistical analyses across multiple benchmark problems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -