为解决多维时间序列的分类并获取易于理解的分类规则,引入了时序熵的概念及构造时序熵的方法,基于属性选择和属性值划分两方面扩展了决策树模型.并给出了两种构造多维时间序列分类的决策树模型算法.最后,采用移动客户流失的真实数据,对过程决策树进行测试,展示了方法的可行性.%To solve the classification problem of multi-dimensional time series and obtain understandable classification rules, the concept of time series entropy and the method of structuring time series entropy were introduced.And the decision tree model was expanded based on both attribute selection and attribute value.Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.Finally, process decision tree was tested on mobile customer churn data, and the feasibility of the proposed method was demonstrated.
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