1. A computer implemented method, comprising: generating, by at least one data processing device, an initial decision tree based on an initial training set, the initial decision tree defining split-rules, each split-rule including at least one clause that specifies a match score that satisfies the clause, the initial training set including training pairs of data records that include positive training pairs that have been identified as duplicate data records and negative training pairs that have not been identified as duplicate data records, each training pair having match scores specifying a measure of similarity for attributes of training pairs; removing, by at least one data processing device and from the split-rules, clauses that are not satisfied by match scores that satisfy threshold match scores to generate initial trimmed rules, the threshold match scores being based on attribute split-points that specify match scores at which the initial training set is segmented; classifying, by at least one data processing device, the negative training pairs in the initial training set based on the match scores for the negative training pairs and the initial trimmed rules; filtering, by at least one data processing device and based on the classification, the initial training set to remove negative training pairs that are classified as duplicate pairs to generate a filtered training set; generating, by at least one data processing device, an intermediate decision tree with the filtered training set, the intermediate decision tree defining Y intermediate split-rules; adjusting, by at least one data processing device, at least a portion of the intermediate split rules to generate final split-rules; and classifying, by at least one data processing device, input pairs of data records as duplicate pairs based on attribute values of the input pairs and the final split-rules.