Affiliation:
1. Department of Mathematics and Statistics, College of Science, Huazhong Agricultural University, Wuhan 430070, P. R. China
Abstract
Record linkage is the task for identifying which records refer to the same entity. When records in different data sources do not have a common key and they contain typographical errors in their identifier fields, the extended Fellegi–Sunter probabilistic record linkage method with consideration of field similarity proposed by Winkler, is one of the most effective methods to perform record linkage to our knowledge. But this method has a limitation that it cannot efficiently handle the problem of missing value in the fields, an inappropriate weight is assigned to record pair containing missing data. Therefore, to improve the performance of Winkler’s probabilistic record linkage method in presence of missing value, we proposed a solution for adjusting record pair’s weight when missing data occurred, which allows enhancing the accuracy of the Winkler’s record linkage decisions without increasing much more computational time.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
1 articles.
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1. A Bayesian record linkage model incorporating relational data;Applied Stochastic Models in Business and Industry;2023-06-26