Learning Rules from Highly Unbalanced Data Sets
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TL;DR
This paper presents a simple and effective rule learning algorithm for highly unbalanced data sets that can conduct an almost exhaustive search for patterns within the known fraudulent cases.
Abstract
This paper presents a simple and effective rule learning algorithm for highly unbalanced data sets. By using the small size of the minority class to its advantage this algorithm can conduct an almost exhaustive search for patterns within the known fraudulent cases. This algorithm was designed for and successfully applied to a law enforcement problem, which involves discovering common patterns of fraudulent transactions.