Benchmarking the Effectiveness and Efficiency of Machine Learning Algorithms for Record Linkage
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Record linkage which refers to the identification of the same entities across several databases in the absence of an unique identifier is a crucial step for data integration. In this research, we study the effectiveness and efficiency of different machine learning algorithms (SVM, Random Forest, and neural networks) to link databases in a controlled experiment. We control for % of heterogeneity in data and size of training dataset. We evaluate the algorithms based on (1) quality of linkages such as F1 score based on a one threshold model and (2) size of uncertain regions that need manual review based on a two threshold model. We find that random forests performed very well both in terms of traditional metrics like F1 score (99.2% - 95.9%) as well as manual review set size (7.1% - 21%) for error rates from 0% to 60%. Though in terms of F1 scores, the algorithms (Random Forests, SVMs and Neural Nets) fared fairly similar, random forests outperformed the next best model by 28% on average in terms of the percentage of pairs that need manual review.
Ilangovan, Gurudev (2019). Benchmarking the Effectiveness and Efficiency of Machine Learning Algorithms for Record Linkage. Master's thesis, Texas A&M University. Available electronically from