基于粗糙集的决策树构造算法

针对ID3算法构造决策树复杂、分类效率不高问题，基于粗糙集理论提出一种决策树构造算法。该算法采用加权分类粗糙度作为节点选择属性的启发函数，与信息增益相比，能全面地刻画属性分类的综合贡献能力，并且计算简单。为消除噪声对选择属性和生成叶节点的影响，利用变精度粗糙集模型对该算法进行优化。实验结果表明，该算法构造的决策树在规模与分类效率上均优于ID3算法。

Decision Tree Constructing Algorithm Based on Rough Set

Aiming at the problems of complex and low efficiency decision tree constructed by ID3, this paper proposes a decision tree classification algorithm based on rough set, which takes the weighted classification rough degree as the heuristic function of choosing attribute at a node. This heuristic function can synthetically measure contribution of an attribute for classification, and is simple in calculation. To eliminate the effect of noise data on choosing attributes and generating leaf nodes, a method using variable precision rough set model is used to optimize the algorithm. Experimental results show that the size of trees generated by the new algorithm is smaller and higher accuracy than ID3 algorithm.