摘要
针对不确定数据的概率分布难以获取的客观实际,讨论了缺失概率分布的值不确定离散对象的决策树。定义了(条件)概率区间,并证明了(条件)概率区间是可达概率区间;基于可达概率区间,定义了(条件)熵区间,并给出了求解(条件)熵区间的上/下界的方法;采用条件熵区间作为属性选择度量,提出了一种新的不确定决策树,将以0-1划分对象的决策树扩展到以概率区间分配对象的决策树,这样不仅可以处理缺失概率分布的值不确定离散对象,也可以处理确定离散对象。通过在基于UCI数据集的不确定数据集上的实验,证实了不确定决策树是有效的。
This paper studies a decision tree for value-uncertain discrete objects missing probabilities, because it is difficult to obtain the probability distributions over uncertain data in applications. Firstly, the paper defines the （con- ditional） probability intervals, and proves that the （conditional） probability intervals are the reachable probability intervals. Secondly, based on the reachable probability intervals, it defines the （conditional） entropy intervals, and gives a method to compute the upper and the lower bounds of the （conditional） entropy intervals. Finally, it presents a new decision tree for uncertain data, in which the conditional entropy intervals are used to select the best attributes and objects are assigned to the branches with probability intervals. The decision tree can handle both value-uncertain discrete objects missing probabilities and certain discrete objects. Experiments with uncertain datasets based on UCI datasets show the satisfactory performance.
出处
《计算机科学与探索》
CSCD
2012年第8期726-740,共15页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金No.61063008
云南省自然科学基金No.2010CD025~~
关键词
缺失概率分布的值不确定离散对象
决策树
可达概率区间
条件熵区间
value-uncertain discrete objects missing probabilities
decision tree
reachable probability interval
con-ditional entropy interval
