一种多变量决策树的构造与研究  

Multi-variable decision tree construction and research

作　　者：陈广花[1] 王正群[1] 刘风[1] 俞振州[1] 

机构地区：[1]扬州大学信息工程学院,江苏扬州225009

出　　处：《计算机工程与应用》2010年第25期217-219,230,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.60875004;No.60774017;江苏省自然科学基金No.BK2009184;江苏省高校自然科学基础研究资助项目No.07KJB520133~~

摘　　要：单变量决策树算法造成树的规模庞大、规则复杂、不易理解,而多变量决策树是一种有效用于分类的数据挖掘方法,构造的关键是根据属性之间的相关性选择合适的属性组合构成一个新的属性作为节点。结合粗糙集原理中的知识依赖性度量和信息系统中条件属性集的离散度概念,提出了一种多变量决策树的构造算法(RD)。在UCI上部分数据集的实验结果表明,提出的多变量决策树算法的分类效果与传统的ID3算法以及基于核方法的多变量决策树的分类效果相比,有一定的提高。Decision tree algorithm in univariate tests causes large-scale, complex rules that are difficult to understand. Multi-variable decision tree is effectively used in the classification of data mining.The key to build it lies in the reasonable choice of attributes combination based on the interconnection between attributes.Based on the rough set theory of attribute dependability and the concept of conditional attributes dispersion degree in information system, a new multi-variable decision tree algorithm called RD is proposed.The results of experiments on the UCI show that the decision tree built by the proposed method has better classification results than those of ID3 algorithm and multi-variate decision tree construction algorithm based on the relative core of attributes.

关 键 词：决策树 粗糙集 属性依赖度 离散度 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]