Rare class discovery based on active learning

Jingrui He, Jaime Carbonell

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

7 Scopus citations

Abstract

In machine learning, the new-class discovery problem remains an open challenge, especially for emergent rare classes. However, the challenge is of crucial importance for applications such as detecting new financial fraud patterns, new viral mutations and new network malware, most of which 'hide' among vast volumes of normal data and observations. This paper focuses on a new approach, based on local-topology density estimation, applicable to discovering examples of the rare classes rapidly, despite non-separability with the majority class(es). The new method, called ALICE, and its variant MALICE, are shown effective both theoretically and empirically in outperforming other methods in the literature, both on challenging synthetic data and on real data sets.

Original languageEnglish (US)
Title of host publication10th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2008
StatePublished - 2008
Externally publishedYes
Event10th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2008 - Fort Lauderdale, FL, United States
Duration: Jan 2 2008Jan 4 2008

Other

Other10th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2008
CountryUnited States
CityFort Lauderdale, FL
Period1/2/081/4/08

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

Cite this

He, J., & Carbonell, J. (2008). Rare class discovery based on active learning. In 10th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2008