Learning Rules from Highly Unbalanced Data Sets

Jianping Zhang; Daniel Venese; Eric Bloedorn; Lowell Rosen

doi:10.1109/ICDM.2004.10015

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - Learning Rules from Highly Unbalanced Data Sets

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/EndNote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Jianping Zhang Articles by Eric Bloedorn Articles by Lowell Rosen Articles by Daniel Venese

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

Learning Rules from Highly Unbalanced Data Sets

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

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.