Abstract
With the rapid development of information technology, various industries have to deal with an increasing number of data. Compared with the traditional static data, stream data under big data environment was rapid, continuous and always changed with time. At the same time, the implicit distribution of data stream brought about the concept drift. A stream data concept drift detection algorithm named ADDS (Anti-concept Drift Detection Algorithm) was put forward, which is mainly used to detect and process the hidden concept drift of unsteady data stream, under big data environment. The ADDS was focused on the improvements of traditional classification algorithms with incremental way to adapt to the demand of streaming data processing. The experimental results showed that the ADDS had a better concept drift detection effect.
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