A Geometric Algorithm for Learning Oblique Decision Trees

Authors:
Naresh Manwani;P. S. Sastry
Affiliations:
Indian Institute of Science, Bangalore, India 12;Indian Institute of Science, Bangalore, India 12
Venue:
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Year:
2009

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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.