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Sampling Methods in Genetic Programming for Classification with Unbalanced Data

  • Rachel Hunt
  • Mark Johnston
  • Will Browne
  • Mengjie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6464)

Abstract

This work investigates the use of sampling methods in Genetic Programming (GP) to improve the classification accuracy in binary classification problems in which the datasets have a class imbalance. Class imbalance occurs when there are more data instances in one class than the other. As a consequence of this imbalance, when overall classification rate is used as the fitness function, as in standard GP approaches, the result is often biased towards the majority class, at the expense of poor minority class accuracy. We establish that the variation in training performance introduced by sampling examples from the training set is no worse than the variation between GP runs already accepted. Results also show that the use of sampling methods during training can improve minority class classification accuracy and the robustness of classifiers evolved, giving performance on the test set better than that of those classifiers which made up the training set Pareto front.

Keywords

Genetic Programming Pareto Front 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rachel Hunt
    • 1
  • Mark Johnston
    • 1
  • Will Browne
    • 2
  • Mengjie Zhang
    • 2
  1. 1.School of Mathematics, Statistics and Operations ResearchVictoria University of WellingtonWellingtonNew Zealand
  2. 2.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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