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The Novelty Detection Approach for Different Degrees of Class Imbalance

  • Hyoung-joo Lee
  • Sungzoon Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

We show that the novelty detection approach is a viable solution to the class imbalance and examine which approach is suitable for different degrees of imbalance. In experiments using SVM-based classifiers, when the imbalance is extreme, novelty detectors are more accurate than balanced and unbalanced binary classifiers. However, with a relatively moderate imbalance, balanced binary classifiers should be employed. In addition, novelty detectors are more effective when the classes have a non-symmetrical class relationship.

Keywords

Support Vector Machine Minority Class Class Imbalance Novelty Detector Support Vector Data Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyoung-joo Lee
    • 1
  • Sungzoon Cho
    • 1
  1. 1.Seoul National UniversitySeoulKorea

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