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An Ensemble Approach for Record Matching in Data Linkage.

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An enhanced data matching strategy combines Fellegi-Sunter (FS) and Jaro-Winkler (JW) techniques. Applying FS after JW improves accuracy, especially with limited demographic data and varied spellings in fields like names and addresses.

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Area of Science:

  • Data Science
  • Information Retrieval
  • Computer Science

Background:

  • Accurate record linkage is crucial for data integration and analysis.
  • Probabilistic data matching techniques like Fellegi-Sunter (FS) and Jaro-Winkler (JW) are widely used.
  • Limitations exist in individual techniques, particularly with noisy or varied data.

Purpose of the Study:

  • To develop and evaluate an optimal ensemble configuration of FS and JW for improved record matching accuracy.
  • To investigate the complementary nature of FS and JW techniques in data matching.
  • To determine the optimal sequence for applying FS and JW in an ensemble.

Main Methods:

  • Ensemble modeling combining Fellegi-Sunter (FS) and Jaro-Winkler (JW) techniques.
  • Comparative analysis of ensemble configurations against individual techniques.
  • Experimental evaluation of matching performance using defined metrics.

Main Results:

  • An ensemble approach combining FS and JW demonstrated improved record matching accuracy.
  • Applying the FS technique to records remaining after JW processing yielded significant improvements.
  • The sequence of applying JW followed by FS was found to be critical for optimal performance.

Conclusions:

  • The developed ensemble approach offers a valuable method for enhancing data matching accuracy.
  • This technique is particularly effective when demographic variables are limited or when fields contain multiple acceptable spellings (e.g., names, addresses).
  • The complementary nature of FS and JW, when applied sequentially, provides a robust solution for challenging data matching scenarios.