Concept Drift Processing Method of Stream Dataing Based on Mixed Feature Extraction

In the era of big data, more and more data are generated in the form of data streams, which makes concept drift an important but difficult problem in stream data mining due to its fast, infinite, unstable and dynamically changing characteristics. Most of the current concept drift processing methods have limited information extraction capability and do not fully consider the temporal features of streaming data. To address these problems, a Processing Method of Streaming Data with Concept Drift Based on Mixed Feature Extraction (MFECD) is proposed, the method first uses convolutional kernels of different scales to model the data to construct splicing features, and uses a gating mechanism to fuse shallow inputs and splicing features for adaptive integration as different network level inputs to obtain data features that can take into account both detailed and semantic information. Based on this, attention mechanism and similarity calculation are used to evaluate the importance of stream data at different moments in order to enhance the temporal features of key site of the data stream. The experimental results show that the method can effectively extract the complex data features and temporal features contained in the streaming data, and improve the processing capability of concept drift in the data stream.