Ensemble classification algorithm for high speed data stream

The algorithms for mining data streams have to make fast response and adapt to the concept drift at the premise of light demands on memory resources.This paper proposed an ensemble classification algorithm for high speed data stream.After dividing a given data stream into several data blocks,it computed the central point and subspace for every class on each block which were integrated as the classification model.Meanwhile,it made use of statistics to detect concept drift.The experimental results show that the proposed method not only classifies the data stream fast and adapt to the concept drift with higher speed,but also has a better classification performance.