On the Stability of Feature Selection Algorithms
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76010564
- Type
- D - Journal article
- DOI
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-
- Title of journal
- Journal of Machine Learning Research
- Article number
- -
- First page
- 1
- Volume
- 18
- Issue
- -
- ISSN
- 1533-7928
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2018
- 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
-
2
- Research group(s)
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A - Computer Science
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Resolves a 20 year open question on the reproducibility of ""feature selection"" algorithms, unifying years of work in the area.
Key result has been adopted by Roche Switzerland (Contact: Group Director, Neuroscience Analytics, PHC Data Science) as an assessment protocol in biomarker selection.
Enabled industry funding Roche Switzerland GBP200,000.
Work is an extended journal version of ""Measuring the Stability of Feature Selection"", ECML 2016, which is also highly cited."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
