Title :
Learning rules from highly unbalanced data sets
Author :
Zhang, Jianping ; Bloedorn, Eric ; Rosen, Lowell ; Venese, Daniel
Author_Institution :
AOL Inc., Dulles, VA, USA
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.
Keywords :
data mining; law administration; learning (artificial intelligence); exhaustive search; fraudulent transaction; law enforcement problem; pattern discovery; rule learning algorithm; unbalanced data sets; Algorithm design and analysis; Classification algorithms; Costs; Data mining; Humans; Inspection; Law enforcement; Spatial databases; Testing; Transaction databases;
Conference_Titel :
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN :
0-7695-2142-8
DOI :
10.1109/ICDM.2004.10015
