• DocumentCode
    2851741
  • Title

    Learning rules from highly unbalanced data sets

  • Author

    Zhang, Jianping ; Bloedorn, Eric ; Rosen, Lowell ; Venese, Daniel

  • Author_Institution
    AOL Inc., Dulles, VA, USA
  • fYear
    2004
  • fDate
    1-4 Nov. 2004
  • Firstpage
    571
  • Lastpage
    574
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
  • Print_ISBN
    0-7695-2142-8
  • Type

    conf

  • DOI
    10.1109/ICDM.2004.10015
  • Filename
    1410363
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2851741