Unbalanced Decision Trees for multi-class classification

@article{Ramanan2007UnbalancedDT,
  title={Unbalanced Decision Trees for multi-class classification},
  author={A. Ramanan and Somjet Suppharangsan and M. Niranjan},
  journal={2007 International Conference on Industrial and Information Systems},
  year={2007},
  pages={291-294}
}
In this paper we propose a new learning architecture that we call unbalanced decision tree (UDT), attempting to improve existing methods based on directed acyclic graph (DAG) and one-versus-all (OVA) approaches to multi-class pattern classification tasks. Several standard techniques, namely one-versus-one (OVO), OVA, and DAG, are compared against UDT by some benchmark datasets from the University of California, Irvine (UCI) repository of machine learning databases. Our experiments indicate that… CONTINUE READING
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Showing 1-10 of 13 references

Harris: “A Kernel-Based Two-Class Classifier for Imbalanced Datasets

  • X. Hong, S. Chen, C.J
  • IEEE Transactions on Neural Networks, vol.18,
  • 2007
1 Excerpt

SVM- a software package for Support Vector Learning

  • T. Joachims
  • 2004.
  • 2004
1 Excerpt

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