属性频率划分和信息熵离散化的决策树算法

决策树是数据挖掘任务中分类的常用方法。在构造决策树的过程中，节点划分属性选择的度量直接影响决策树分类的效果。基于粗糙集的属性频率函数方法度量属性重要性，并用于分枝划分属性的选择和决策树的预剪枝，提出一种决策树学习算法。同时，为了能处理数值型属性，利用数据集的统计性质为启发式知识，提出了一种改进的数值型属性信息熵离散化算法。实验结果表明，新的离散化方法计算效率有明显提高，新的决策树算法与基于信息熵的决策树算法相比较，结构简单，且能有效提高分类效果。

Decision tree algorithm using attribute frequency splitting and information entropy discretization

Decision tree is a usual method of classification in data mining.In the process of constructing a decision tree,the criteria of selecting partition attributes will influence the efficiency of classification.Based on the concept of attributes importance metric that is measured by a function of attribute frequency in Rough Set theory,and which is used to select the partition at-tribute and pre-prune the decision tree,a new decision tree algorithm is proposed.In addition,using the heuristics information of dat...