BayesPiles: Visualisation Support for Bayesian Network Structure Learning
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1218452
- Type
- D - Journal article
- DOI
-
10.1145/3230623
- Title of journal
- ACM transactions on intelligent systems and technology
- Article number
- 5
- First page
- 1
- Volume
- 10
- Issue
- 1
- ISSN
- 2157-6904
- Open access status
- Compliant
- Month of publication
- November
- 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
-
5
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a visualisation technique to assist analysts using machine learning techniques in determining the best network solution for their problem. The technique assists the analyst in understanding the exploration space and in determining a consensus network. The work was a collaboration with computational biologists exploring ways to determine best fit models for biological processes.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -