A System for Induction of Oblique Decision Trees
A System for Induction of Oblique Decision Trees
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DOI：
10.1613/jair.63
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发表时间：
1994-01-01
影响因子：
5
通讯作者：
Salzberg, Steven
中科院分区：
文献类型：
--
作者：
Murthy, Sreerama K.;Kasif, Simon;Salzberg, Steven
本文介绍了一种新的系统，用于诱导斜决策树。这个系统OC 1结合了确定性爬山和两种形式的随机化，在决策树的每个节点上找到一个好的斜分裂（以超平面的形式）。倾斜决策树方法特别针对属性为数值的域进行了调整，尽管它们可以适用于符号或混合符号/数值属性。我们提出了广泛的实证研究，使用真实的和人工数据，分析OC 1的能力，构建斜树，更小，更准确的比他们的轴平行的同行。我们还研究了斜决策树的建设随机化的好处。
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.