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.
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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 |
|---|---|
| Series.: | Lecture Notes in Computer Science |
| Publisher: | Springer |
| Additional Information: | Copyright of this article belongs to Springer. |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 15 Jul 2010 09:14 |
| Last Modified: | 25 Oct 2018 14:31 |
| URI: | http://eprints.iisc.ac.in/id/eprint/28983 |
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