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Publication - Dr Valeriia Haberland

    Improving Record Linkage Accuracy with Hierarchical Feature Level Information and Parsed Data

    Citation

    Zhou, Y, Wang, M, Haberland, V, Howroyd, J, Danicic, S & Bishop, M, 2017, ‘Improving Record Linkage Accuracy with Hierarchical Feature Level Information and Parsed Data’. New Generation Computing, vol 35., pp. 87?104

    Abstract

    Probabilistic record linkage is a well established topic in the literature. Fellegi–Sunter probabilistic record linkage and its enhanced versions are commonly used methods, which calculate match and non-match weights for each pair of records. Bayesian network classifiers–naive Bayes classifier and TAN have also been successfully used here. Recently, an extended version of TAN (called ETAN) has been developed and proved superior in classification accuracy to conventional TAN. However, no previous work has applied ETAN to record linkage and investigated the benefits of using naturally existing hierarchical feature level information and parsed fields of the datasets. In this work, we extend the naive Bayes classifier with such hierarchical feature level information. Finally we illustrate the benefits of our method over previously proposed methods on four datasets in terms of the linkage performance (F1 score). We also show the results can be further improved by evaluating the benefit provided by additionally parsing the fields of these datasets.

    Full details in the University publications repository