Aggregating crowdsourced quantitative claims : additive and multiplicative models
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 252072137
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2016.2535383
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 1621
- Volume
- 28
- Issue
- 7
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- 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
-
4
- Research group(s)
-
A - Artificial Intelligence
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This publication is from the project "Collaborative Intelligence Analysis" led by Norman, a collaboration with UCLA and Honeywell, funded by UK-MoD and US-DoD through the NIS-ITA programme (http://nis-ita.org/). The project focussed on developing techniques to support analytics for military intelligence, where analysis of open and crowdsourced data to identify potential events of interest is a significant challenge. However, estimating quantities is hard for individuals. Our model focuses on a reliable estimation of quantities and ratios from low-quality and conflicting estimations. Research from the general project was identified by US and UK governments as a key highlight from the 10-year programme.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
