11. The computer-implemented method of claim 5 , wherein the first machine learning model is a genetic algorithm (GA) model, wherein identifying one or more false positive individuals comprises determining that one or more rules of the genetic algorithm model identify an individual as meeting said specific criteria while the label of that individual indicates that individual does not meet said specific criteria.
11. The computer-implemented method of claim 5 , wherein the first machine learning model is a genetic algorithm (GA) model, wherein identifying one or more false positive individuals comprises determining that one or more rules of the genetic algorithm model identify an individual as meeting said specific criteria while the label of that individual indicates that individual does not meet said specific criteria. 12. The computer-implemented method of claim 11 , wherein selecting one or more of the identified false positives as candidates to be corrected within the reference data comprises: during the generation of the genetic algorithm model, creating count values for multiple false positive individuals and incrementing a given false positive individual's count value each time a rule of the genetic algorithm model incorrectly identifies that individual as a duplicate; and selecting one or more false positive individuals as candidates to be corrected based on the count values.