基于粗集和距离函数的决策树构造方法  

Decision tree construction method based on rough set and distance function

作　　者：陈世联[1] 罗秋瑾[2] 

机构地区：[1]淮阴工学院计算科学系,江苏淮安223002 [2]云南财经大学计算机科学系,云南昆明650221

出　　处：《计算机工程与设计》2008年第12期3191-3193,共3页Computer Engineering and Design

基　　金：淮阴工学院科研基金项目(121710)

摘　　要：决策树是一种有效用于分类的数据挖掘方法。在决策树构造算法中,粗集理论的相对核已被应用于解决多变量检验中属性的选择问题。考虑到决策树技术和粗集的优缺点,将二者结合起来,先对每个结点包含的属性个数加以限制,再用属性相关度和DeMantaras距离函数选择相关的属性组合作为属性选择的标准,给出一种新的构造算法。该算法的优点是能有效降低树的高度,而且增强了分类规则的可读性。Decision tree which is effectively used in the classification of data mining. On the decision tree construction algorithm, the core of condition attributes with respect to decision attributes in rough set theory is used for selection of attributes in multivariate tests. Considering the advantage and disadvantage of the decision trees and rough set, the decision trees and rough set are combined. A new multivariate decision tree construction algorithm is offered, which restricts each of nodes containing the number of attributes, and then, choice attributes combination according to the attribute dependability and De Mantaras distance function. The prominent merit of this algorithm is that it can reduce the height of a tree, and can raise readability of classing rules.

关 键 词：粗集 决策树 条件属性 属性依赖度 DeMantaras距离函数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]