Rare category characterization

Jingrui He, Hanghang Tong, Jaime Carbonell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations


Rare categories abound and their characterization has heretofore received little attention. Fraudulent banking transactions, network intrusions, and rare diseases are examples of rare classes whose detection and characterization are of high value. However, accurate characterization is challenging due to high-skewness and non-separability from majority classes, e.g., fraudulent transactions masquerade as legitimate ones. This paper proposes the RACH algorithm by exploring the compactness property of the rare categories. It is based on an optimization framework which encloses the rare examples by a minimum-radius hyperball. The framework is then converted into a convex optimization problem, which is in turn effectively solved in its dual form by the projected subgradient method. RACH can be naturally kernelized. Experimental results validate the effectiveness of RACH.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Number of pages10
StatePublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other10th IEEE International Conference on Data Mining, ICDM 2010
CitySydney, NSW


  • Characterization
  • Compactness
  • Hyperball
  • Minority class
  • Optimization
  • Rare category
  • Subgradient

ASJC Scopus subject areas

  • Engineering(all)


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