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A Geometric Algorithm for Learning Oblique Decision Trees

  • Naresh Manwani
  • P. S. Sastry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

Keywords

Support Vector Machine Decision Tree Gini Index Generalize Eigenvalue Problem Decision Tree Algorithm 
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 2009

Authors and Affiliations

  • Naresh Manwani
    • 1
  • P. S. Sastry
    • 1
  1. 1.Indian Institute of ScienceBangaloreIndia

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