Abstract: Embodiments of a system and method for generating a classification model with a cost function having different penalties for false positives and false negatives are described. Embodiments may include perform machine learning operations on known duplicates and known non-duplicates to generate a classification model for classifying structured data items as duplicates or non-duplicates. Each duplicate may represent a pair of structured data items describing a common item; each non-duplicate may represent a pair of structured data items describing different items. Generation of the classification model may be performed based on a cost function that penalizes false positive misclassifications within the classification model differently than false negative misclassifications. Embodiments may also include evaluating the classification model to determine whether a candidate structured data item is a duplicate or non-duplicate.

Abstract: Embodiments of a system and method for seeding rule-based machine learning models include generating a set of seed rules including one or more rules resulting from one or more previously performed machine learning operations and one or more randomly or pseudo-randomly generated rules. Embodiments may include performing one or more machine learning operations on the set of seed rules to generate a new set of rules for determining whether a pair of information items have a specific relationship. Generating the new set of rules from the seed rules may be faster than generating a set of rules from random data. Embodiments may also include applying the new set of rules to one or more pairs of information items to identify at least one pair of information items as having the specific relationship. For instance, the rules may be applied to identify pairs of information items that are duplicate pairs.