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Optimal F-score Matching for Bipartite Record Linkage
Eric A Bai1, Olivier Binette2, Jerome P Reiter2
1Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA.
Statistics and Computing
|March 30, 2026
Summary
This study introduces a new estimator to improve probabilistic record linkage accuracy. The novel approach maximizes the expected F-score, enhancing the matching of records between files with potential errors.
Area of Science:
- Statistics
- Data Science
- Computer Science
Background:
- Probabilistic record linkage is crucial for matching records across datasets, especially when identifiers like names have errors.
- Bipartite record linkage scenarios involve matching records between two files without internal duplicates, where entities may exist in both files.
Purpose of the Study:
- To introduce a novel estimator for probabilistic record linkage that optimizes the F-score.
- To provide a point estimate for the linkage structure, ensuring each record is matched to at most one record in the other file.
Main Methods:
- Developed an estimator that maximizes the expected F-score for the linkage structure.
- Targeted methods producing posterior distributions or match probabilities for record pairs.
Main Results:
- The proposed F-score estimator demonstrates desirable properties in simulations.
- Applications with real-world data validate the effectiveness of the F-score estimators.
Conclusions:
- The developed F-score maximization estimator offers an improved approach to probabilistic record linkage.
- This method is suitable for linkage techniques that provide probabilistic outputs, enhancing matching accuracy.
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