High-speed Stream Integrated Classification Method Based on Bias Sampling

High-speed data stream is a data flow velocity which exceeds the processing power of an integrated classifier; integrated classifier training cannot reach all the most recent data to update the classification model. To this end, this paper introduces the theory of optimal Bayesian classifier, and analyzed on the basis of its integrated classifier expect the deviation error variance decomposition, and finally presents a sampling bias based on integrated high-speed data stream classification algorithm, theoretical analysis is the experimental verification show the algorithm can effectively reduce the integrated classifier training update at the same time, the classification remains a high classification performance.