Learning Rules from Highly Unbalanced Data Sets

Authors:
Jianping Zhang;Eric Bloedorn;Lowell Rosen;Daniel Venese
Affiliations:
AOL, Inc., Dulles, VA;MITRE Corporation, McLean, Virginia;MITRE Corporation, McLean, Virginia;MITRE Corporation, McLean, Virginia
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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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.