A System for Induction of Oblique Decision Trees
| dc.creator | Murthy, S. K. | |
| dc.creator | Kasif, S. | |
| dc.creator | Salzberg, S. | |
| dc.date | 1994-08-01 | |
| dc.date.accessioned | 2026-07-07T09:12:18Z | |
| dc.date.available | 2026-07-07T09:12:18Z | |
| dc.description | 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. | |
| dc.description | See http://www.jair.org/ for an online appendix and other files accompanying this article | |
| dc.identifier | https://arxiv.org/abs/cs/9408103 | |
| dc.identifier | http://arxiv.org/abs/cs/9408103 | |
| dc.identifier | Journal of Artificial Intelligence Research, Vol 2, (1994), 1-32 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151980 | |
| dc.subject | Artificial Intelligence | |
| dc.title | A System for Induction of Oblique Decision Trees | |
| dc.type | text |