不确定数据的决策树分类算法

经典决策树算法不能处理树构建和分类过程中的不确定数据。针对这一局限，将可用于不确定数据表达的证据理论与决策树分类算法相结合，把决策树分类技术扩展到含有不确定数据的环境中。为避免在决策树构建过程中出现组合爆炸问题，引入新的测量算子和聚集算子，提出了D-S证据理论决策树分类算法。实验结果表明，D-S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。

Uncertain data decision tree classification algorithm

Classic decision tree algorithm is unfit to cope with uncertain data pervaded at both the construction and classification phase. In order to overcome these limitations, D-S decision tree classification algorithm was proposed. This algorithm extended the decision tree technique to an uncertain environment. To avoid the combinatorial explosion that would result from tree construction phase, uncertainty measure operator and aggregation combination operator were introduced. This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.