Learning Rules from Highly Unbalanced Data Sets

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Jianping Zhang, Eric Bloedorn, Lowell Rosen, Daniel Venese, "Learning Rules from Highly Unbalanced Data Sets," Data Mining, IEEE International Conference on, pp. 571-574, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10015, author = {Jianping Zhang and Eric Bloedorn and Lowell Rosen and Daniel Venese}, title = {Learning Rules from Highly Unbalanced Data Sets}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {571-574}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10015}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Learning Rules from Highly Unbalanced Data Sets SN - 0-7695-2142-8 SP571 EP574 A1 - Jianping Zhang, A1 - Eric Bloedorn, A1 - Lowell Rosen, A1 - Daniel Venese, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Jianping Zhang, AOL, Inc., Dulles, VA

Eric Bloedorn, MITRE Corporation, McLean, Virginia

Lowell Rosen, MITRE Corporation, McLean, Virginia

Daniel Venese, MITRE Corporation, McLean, Virginia

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10015

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.

Citation:

Jianping Zhang, Eric Bloedorn, Lowell Rosen, Daniel Venese, "Learning Rules from Highly Unbalanced Data Sets," icdm, pp.571-574, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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