Manwani, Naresh and Sastry, PS (2009) A Geometric Algorithm for Learning Oblique Decision Trees. In: 3rd International Conference on Pattern Recognition and Machine Intelligence, DEC 16-20, 2009, IIT Delhi, pp. 25-31.Full text not available from this repository.
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.
|Item Type:||Conference Proceedings|
|Additional Information:||Copyright of this article belongs to Springer.|
|Date Deposited:||15 Jul 2010 09:14|
|Last Modified:||20 Mar 2012 07:19|
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