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Local Characteristics of Minority Examples in Pre-processing of Imbalanced Data

  • Jerzy Stefanowski
  • Krystyna Napierała
  • Małgorzata Trzcielińska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

Informed pre-processing methods for improving classifiers learned from class-imbalanced data are considered. We discuss different ways of analyzing the characteristics of local distributions of examples in such data. Then, we experimentally compare main informed pre-processing methods and show that identifying types of minority examples depending on their k nearest neighbourhood may help in explaining differences in performance of these methods. Finally, we exploit the information about the local neighbourhood to modify the oversampling ratio in a SMOTE–related method.

Keywords

Local Characteristic Majority Class Local Neighbourhood Minority Class Class Imbalance 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Jerzy Stefanowski
    • 1
  • Krystyna Napierała
    • 1
  • Małgorzata Trzcielińska
    • 1
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

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