Mathematical programming for piecewise linear regression analysis
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 111334229
- Type
- D - Journal article
- DOI
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10.1016/j.eswa.2015.08.034
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 156
- Volume
- 44
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- 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)
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-
- Citation count
- 37
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Model (OPLRA) tested via state-of-the-art validation and cited in works related to financial and social modelling, pharmacology and theoretical computer science. It was used as one of 10 benchmark methods in Yang et al, IEEE Access, 7, 29845, 2019, where it was stated “Generally, as piecewise regressors, both OPLRA and PlrPC outperform than other global regression methods”. The model was employed for drug discovery (Cardoso-Silva, et al. Molecular Informatics 2019 and Cardoso-Silva, et al. Journal of Computer-Aided Molecular Design 2019) and was further developed in Yang, et al. Expert Systems with Applications 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -