【摘要】 决策树方法是一种重要的可完成分类任务的知识发现技术 ,目的是通过构造一个分类模型 ,把数据库中的元组映射到给定类别中的某一个。决策树分类算法效率高且应用广泛 ,但是不能处理在决策树的构建和分类过程中的不确定数据。针对决策树分类算法的局限 ,利用证据理论是对概率论的扩展 ,将置信函数与概率的上下值相联系 ,可用于不确定数据的表达这个有力工具 ,把决策树分类技术扩展到含有不确定数据的环境中 ,提出了 D- S决策树分类算法。实验结果表明 D- S决策树分类算法能有效的对不确定数据进行分类。更多还原

【Abstract】 The decision tree is an important technique for accomplishing the classification task of knowledge discovery.The target of the classification is to find out a classification model.The model can map a single record in database to a pre-assumed class.Despite its popularity and efficiency,the standard decision tree is unfit to cope with data pervaded with uncertainty both at the construction and at the classification phase.Dempster-Shafer evidence theory extends from the probability theory and can handle uncertain data.In order to overcome these limitations encountered in a standard decision tree,this paper extends the decision tree technique to an uncertain environment where the uncertain is represented by evidence theory.This D-S decision tree is a new classification method adapted to the uncertain data.更多还原