摘要
采用粗糙集理论和属性值聚类相结合的方法,从决策树最优化的三个原则对其进行优化。首先,采用粗糙集理论的约简功能求出相对核,并利用信息熵作为启发信息求出相对约简,以此来保证生成决策树的路径最短和减少决策树的节点数。其次,在选择特征属性时,在信息熵增益最大的前提下,根据属性值间的相异性距离来对属性值聚类使其能够接近单峰分布。通过对UCI数据实验分析,结果表明很大程度上减少了决策树的节点数和决策树的深度。
The paper puts forward the way which includes the rough set theory and the cluster of attribute＇s values by optimizing decision tree from three principles.First,the relative core and relative reduction based on information entropy is worked out by rough set theory,which decreasing the decision tree＇s nodes in number and the decision tree＇s path in depth.Second,when the characteristic attributes that the information entropy is most gained are selected,by clustering attribute＇s values,the curve shows the peak of distribution,or approximately.By analyzing the data of UCI database,the results show that the algorithm greatly decreases decision tree＇s nodes in number and the depth of the paths.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第31期178-181,共4页
Computer Engineering and Applications
关键词
粗糙集理论
决策树
属性约简
ID3算法
信息熵
Rough Set
decision, tree
attribute reduction
ID3 algorithm
information entropy
