摘要
针对ID3等传统的决策树算法通常采用单个属性作为分枝判断依据,导致生成树的规模大、形成的规则较难理解的问题,提出了一种以多变量作为结点属性判断条件的算法.该算法利用粗糙集中属性依赖的特性,选择信息系统中条件属性相对决策属性的核属性作为多变量结点属性,使用相对泛化的概念辅助分枝过程,进而生成多变量决策树.通过实例分析与传统的ID3算法进行比较,证明了改进算法的高效性.
The traditional decision tree algorithms such as ID3 usually uses a single attribute as the basis of branching judgment.The scale of the tree generated by ID3 is very large and rules formed are difficult to understand.Aiming at the problems described above,an algorithm was proposed using multi-variable as the judging conditions of node attributes.By using the property of attribute dependency in rough set and choo-sing nuclear properties of condition attributes relative to decision attributes in the information system as multi-variable node attributes,the algorithm used the concept of relative generalization to aid the branching process and generated a multi-variable decision tree.Through the analysis of example and by comparing with the conventional ID3 algorithm,the high efficiency of the improved algorithm was verified.
出处
《郑州轻工业学院学报（自然科学版）》
CAS
2015年第1期50-54,共5页
Journal of Zhengzhou University of Light Industry:Natural Science
基金
河南省科技攻关计划项目(122102210492)
关键词
粗糙集
ID3算法
决策树
相对泛化
等价关系
rough set
ID3 algorithm
decision tree
relative generalization
equivalent relationship
