基于自适应快速决策树的不确定数据流概念漂移分类算法

Classifying algorithm for concept drifting of uncertain data streams based on adapting fast decision tree algorithm

由于不确定数据流中一般隐藏着概念漂移问题,对其进行有效分类存在着很多困难.为此,提出一种基于自适应快速决策树的算法.该算法基于一般决策树算法的原理,以自适应学习规则计算信息增益,以无标记情景学习拆分原理检测不确定数据流中的不确定数值属性,通过自适应快速决策树节点的拆分方法将不确定数值属性转化为不确定分类属性,以实现对不确定数据流的有效分类,进而有效检测到其中隐含的概念漂移现象.仿真结果验证了所提出方法的可靠性.

Because of the concept drift problem hidden in the uncertain data stream, it is very difficult to classify them effectively. Based on the general decision tree algorithm, the adaptive fast decision tree algorithm can count information gain based on the adaptive learning rule, and detect uncertain numerical attributes though the principle of the non-marking learning scene. The numerical attribute is transformed into a non-determined classification attribute by using splitting method,so classification of uncertain data stream is realized effectively. Then the concept drift phenomenon is effectively detected in the uncertain data stream. Simulation results show the reliability of the proposed method.