# An oversampling-undersampling strategy for large-scale data linkage

## TL;DR

This paper introduces a method to improve record linkage in big data by balancing imbalanced datasets through oversampling and undersampling.

## Contribution

The novel approach combines oversampling and undersampling to address data imbalance in large-scale record linkage.

## Key findings

- The strategy improves accuracy and efficiency in record linkage for imbalanced datasets.
- Sensitivity tests showed effectiveness under varying training-test ratios and imbalance levels.

## 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.