Work

An oversampling-undersampling strategy for large-scale data linkage

Year: 2025

Type: article

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.

Source: Frontiers in Big Data

DOI: 10.3389/fdata.2025.1542483

Authors: Hossein Hassani, Mohammad Reza Entezarian, Sara Zaeimzadeh, Leila Marvian, Nadejda Komendantova

Institutions: International Institute for Applied Systems Analysis, Shahid Beheshti University, University of Tehran, Imam Reza International University

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,418