Compact Ensemble Trees for Imbalanced Data

  • Yubin Park
  • Joydeep Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


This paper introduces a novel splitting criterion parametrized by a scalar ‘α’ to build a class-imbalance resistant ensemble of decision trees. The proposed splitting criterion generalizes information gain in C4.5, and its extended form encompasses Gini(CART) and DKM splitting criteria as well. Each decision tree in the ensemble is based on a different splitting criterion enforced by a distinct α. The resultant ensemble, when compared with other ensemble methods, exhibits improved performance over a variety of imbalanced datasets even with small numbers of trees.


Ensemble Method Imbalanced Data Splitting Criterion Imbalanced Dataset Class Imbalance Problem 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yubin Park
    • 1
  • Joydeep Ghosh
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of TexasAustinUSA

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