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A Decision-Tree Framework for Instance-space Decomposition

  • Shahar Cohen
  • Lior Rokach
  • Oded Maimon
Part of the Studies in Computational Intelligence book series (SCI, volume 23)

Summary

This paper presents a novel instance-space decomposition framework for decision trees. According to this framework, the original instance-space is decomposed into several subspaces in a parallel-to-axis manner. A different classifier is assigned to each subspace. Subsequently, an unlabelled instance is classified by employing the appropriate classifier based on the subspace where the instance belongs. An experimental study which was conducted in order to compare various implementations of this framework indicates that previously presented implementations can be improved both in terms of accuracy and computation time.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shahar Cohen
    • 1
  • Lior Rokach
    • 2
  • Oded Maimon
    • 1
  1. 1.Department of Industrial EngineeringTel-Aviv UniversityIsrael
  2. 2.Department of Information Systems EngineeringBen-Gurion University of the NegevIsrael

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