Integer Linear Programming for the Bayesian network structure learning problem
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54875172
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2015.03.003
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 258
- Volume
- 244
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- 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
-
1
- Research group(s)
-
-
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper examines methods for improving the performance of an integer programming approach to learning Bayesian networks from data and provides a practical implementation of the methods. The software associated with this paper (GOBNILP) has been downloaded more than 350 times since 2017 and has been used in over 60 publications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -