  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.
