| 摘要: |
| 二分离散化是决策树生成中处理连续属性最常用的方法,对于连续属性较多的问题,生成的决策树庞大,知识表示难以理解.针对两类分类问题,提出一种基于属性变换的多区间离散化方法--RCAT,该方法首先将连续属性转化为某类别的概率属性,此概率属性的二分法结果对应于原连续属性的多区间划分,然后对这些区间的边缘进行优化,获得原连续属性的信息熵增益,最后采用悲观剪枝与无损合并剪枝技术对RCAT决策树进行简化.对多个领域的数据集进行实验,结果表明:对比二分离散化,RCAT算法的执行效率高,生成的决策树在保持分类精度的同时,树的规模小,可理解性强. |
| 关键词: 机器学习 二分决策树 信息熵增益 剪枝 RCAT算法 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.69825104 (国家自然科学基金) |
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| Constructing Binary Classification Trees with High Intelligibility |
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JIANG Yan-Huang,YANG Xue-Jun,ZHAO Qiang-Li
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| Abstract: |
| Binarization is the most popular discretization method in decision tree generation, while for the domain with many continuous attributes, it always gets a big incomprehensible tree which can't be described as knowledge. In order to get a more intelligible decision tree, this paper presents a new discretization algorithm, RCAT, for continuous attributes in the generation of binary classification tree. It uses simple binarization to solve the multisplitting problem through mapping a continuous attribute into another probability attribute based on statistic information. Two pruning methods are introduced to simplify the constructed tree. Empirical results of several domains show that, for the two-class problem with a preponderance of continuous attributes, RCAT algorithm can generate a much smaller decision tree efficiently with higher intelligibility than binarization while retaining predictive accuracy. |
| Key words: machine learning binary classification tree information gain pruning range-splitting based on continuous attributes transform (RCAT) algorithm |
