摘要
采用粗糙集理论中的属性重要度作为挑选测试属性的指标来构造决策树,形成了一种新的决策树分类算法S_D_Tree,在计算挑选测试属性的时间复杂度为O(|C||n|)。实验结果表明,该算法可以构建一个较简洁的决策树,与C4.5算法相比较,具有更好的预测准确率。
In this paper we use the significance of the attribute in rough set theory as the index to select splitting attributes for constructing the decision tree,and put forward a new decision tree classification algorithm S_D_Tree,of which the time complexity for selecting splitting attribute is O（｜C｜｜n｜）.Experimental results on three data sets demonstrate that the proposed algorithm can construct a less complex decision tree,and can also obtain comparative classification accuracy compared with C4.5.
出处
《计算机应用与软件》
CSCD
2011年第2期80-82,共3页
Computer Applications and Software
基金
江苏省"青蓝工程"
六大人才高峰(07-E-025)
江苏省高校自然科学重大基金研究(08KJA520001)
关键词
决策树
粗糙集
属性重要度
时间复杂度
Decision tree Rough set Significance of attribute Time complexity
