A Novel Ensemble Learning Approach to Unsupervised Record Linkage
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 135794708
- Type
- D - Journal article
- DOI
-
10.1016/j.is.2017.06.006
- Title of journal
- Information Systems
- Article number
- -
- First page
- 40
- Volume
- 71
- Issue
- Nov 2017
- ISSN
- 0306-4379
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- 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
-
3
- Research group(s)
-
C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Record linkage is a process of identifying records that refer to the same real-world entity. Machine learning techniques can classify a pair of records as either match or non-match. The main requirement of such an approach is a labelled training dataset. In many real-world applications no labelled dataset is available hence supervised learning is required. When evaluated on 4 publicly available datasets which are commonly used in the record linkage community, we showed that our unsupervised approach achieves comparable results to those of the supervised approach. This work makes a significant step towards Big Data integration.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
