流数据概念漂移的检测算法

Detecting algorithm of concept drift from stream data

鉴于流数据具有实时、连续、有序和无限等特点,使用近似方法便可检测连续分时段的流数据序列,基于此,运用目标分布数据,结合相似分布理论,提出了利用Tr-OEM算法对流数据中的概念漂移现象进行检测.该算法能够动态地判断流数据概念漂移的发生,自适应地优化概念漂移的检测值,适用于不同类型的流数据.通过分析和实验仿真可以表明,该算法在处理流数据概念漂移时具有较好的适应性.

Based on the stream data with the characters such as real-time, continuous, orderly and unlimited, the continuous- time data sequence can be detected by using the approximate method. Based on this, making use of samples not only from the target distribution but also from similar distributions, Tr-OEM algorithm is proposed to detect the concept drift phenomenon in stream data. This algorithm dynamically estimates the occurrence of concept drift in stream data, automatically determines optimizing or reconstructing classifiers, and is applied to different types of stream data. The analysis and simulation experiments Show that the proposed algorithm has better adaptability while handling the concept drift in stream data.