Evaluation of record linkage methods for iterative insertions

There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies.

The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an empirical evaluation on artificial and real data in the context of iterative insertions. This evaluation provides a deeper insight into empirical similarities and differences between different modelling frames of the record linkage problem. In addition, the effects of using string comparators on the performance of different matching algorithms are evaluated.

Our central results show that stochastic record linkage based on the principle of the EM algorithm exhibits best classification results when calibrating data are structurally different to validation data. Bagging, boosting together with support vector machines are best classification methods when calibrating and validation data have no major structural differences.

The most promising methodologies for record linkage in environments similar to the one considered in this paper seem to be stochastic ones.