A System for Induction of Oblique Decision Trees
<|reference_start|>A System for Induction of Oblique Decision Trees: 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.<|reference_end|>
@article{murthy1994a,
    title={A System for Induction of Oblique Decision Trees},
    author={S. K. Murthy, S. Kasif, S. Salzberg},
    journal={Journal of Artificial Intelligence Research, Vol 2, (1994), 1-32},
    year={1994},
    archivePrefix={arXiv},
    eprint={cs/9408103},
    primaryClass={cs.AI}
}