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Dealing with Data Difficulty Factors While Learning from Imbalanced Data

  • Jerzy StefanowskiEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 605)

Abstract

Learning from imbalanced data is still one of challenging tasks in machine learning and data mining. We discuss the following data difficulty factors which deteriorate classification performance: decomposition of the minority class into rare sub-concepts, overlapping of classes and distinguishing different types of examples. New experimental studies showing the influence of these factors on classifiers are presented. The paper also includes critical discussions of methods for their identification in real world data. Finally, open research issues are stated.

Keywords

Support Vector Machine Classification Performance Majority Class 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.

Notes

Acknowledgments

The research was funded by the the Polish National Science Center, grant no. DEC-2013/11/B/ST6/00963. Close co-operation with Krystyna Napierala in research on types of examples is also acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

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