针对经典决策树算法构造的决策树结构复杂、缺乏对噪声数据适应能力等局限性,基于多尺度粗糙集模型提出一种新的决策树构造算法.算法引入尺度变量和尺度函数概念,采用不同尺度下近似分类精度选择测试属性构造决策树,使用抑制因子对决策树进行修剪,有效地去除了噪声规则.结果表明该算法构造的决策树简单有效,对噪声数据有一定的抗干扰性,且能满足不同用户对决策精度的要求.%Concerning the complicated structure and being sensitive to noise and other problems of decision tree constructed by classical decision tree algorithms, a new decision tree construction algorithm based on multiscale rough set model was proposed. This proposed algorithm introduced the concept of scale variable and scale function, the index of approximate classification accuracy in different scales was used to select test attributes and the hold-down factor was put forward to prune the decision tree and removed the noise rules effectively. The results show that decision tree constructed by this algorithm is simple, and has certain degree of anti-interference and can meet the decision accuracy requirements from different users.
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