Evaluating Genetic Algorithms for selection of similarity functions for record linkage

Machine learning algorithms have been successfully employed in solving the record linkage problem. Machine learning casts the record linkage problem as a classification problem by training a classifier that classifies 2 records as duplicates or unique. Irrespective of the machine learning algorithm used, the initial step in training a classifier involves selecting a set of similarity functions to be applied to each attribute to get a similarity measure. Usually this is done manually with input from a domain expert. We evaluate an approach in which the optimal combination of similarity function for a given type of input data records is searched using Genetic Algorithms.