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:: Volume 16, Issue 1 (9-2022) ::
JSS 2022, 16(1): 1-24 Back to browse issues page
Record Linkage with Machine Learning Methods
Zahra Rezaei Ghahroodi * , Zhina Aghamohamadi
Abstract:   (1978 Views)

With the advent of big data in the last two decades, in order to exploit and use this type of data, the need to integrate databases for building a stronger evidence base for policy and service development is felt more than ever. Therefore, familiarity with the methodology of data linkage as one of the methods of data integration and the use of machine learning methods to facilitate the process of recording records is essential. In this paper, in addition to introducing the record linkage process and some related methods, machine learning algorithms are required to increase the speed of database integration, reduce costs and improve record linkage performance. In this paper, two databases of the Statistical Center of Iran and Social Security Organization are linked.

Keywords: Record Linkage, Machine Learning, Fellegi-Sunter Model, Jaro and Winkler String Comparison, Official Statistics.
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Type of Study: Applied | Subject: Official Statistics
Received: 2021/12/16 | Accepted: 2022/09/1 | Published: 2022/08/2
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Rezaei Ghahroodi Z, Aghamohamadi Z. Record Linkage with Machine Learning Methods. JSS 2022; 16 (1) :1-24
URL: http://jss.irstat.ir/article-1-789-en.html


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Volume 16, Issue 1 (9-2022) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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