An oversampling-undersampling strategy for large-scale data linkage - Hassani, Hossein; Entezarian, Mohammad Reza; Zaeimzadeh, Sara; Marvian, Leila; Komendantova, Nadejda (Frontiers in Big Data, 2025)
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.