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A System for Induction of Oblique Decision Trees

S. K. Murthy·S. Kasif·S. Salzberg
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Abstract

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

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S. K. Murthy, S. Kasif, S. Salzberg. 1994-08-01. A System for Induction of Oblique Decision Trees. https://arxiv.org/abs/cs/9408103

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