An oversampling-undersampling strategy for large-scale data linkage

Hassani, H., Entezarian, M.R., Zaeimzadeh, S., Marvian, L., & Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179 (2025). An oversampling-undersampling strategy for large-scale data linkage. Frontiers in Big Data 8 10.3389/fdata.2025.1542483.

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Abstract

Effective record linkage in big data, particularly in imbalanced datasets, is a critical yet highly challenging task due to the inherent complexity involved. This article utilizes an oversampling-undersampling strategy to address linkage imbalances, enabling more accurate and efficient record linkage within large-scale datasets. It tries to increase the instances of the minority class and decrease the dominance of the majority classes to try to reach a more balanced dataset that can be used for training and testing. Sensitivity testing was carried out by varying the training-test ratio and degree of imbalance.

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