Abstract: With the rapid development of the Internet environment, the concept drift may exist in the network data stream. The classification of the data stream has changed from the traditional static classification to the dynamic classification. The key of dynamic classification is how to detect the concept drift. In this paper, an adaptive classification algorithm for network data streams based on concept drift detection is proposed. The algorithm detects concept drift by comparing the differences of distribution difference between the data in the sliding window and historical data, and then the window data is oversampled to reduce the imbalance between the samples, finally, the processed data sets are input into OS-ELM classifier for online learning, it updates the classifier to cope with the concept drift in the data stream. In this paper, the proposed algorithm is tested on the MOA experimental platform by using synthetic data sets and real data sets. The results show that the classification accuracy and stability of the algorithm are improved compared with the traditional ensemble learning algorithm, and with the increase of data flow, the advantage of time performance begins to show, which is suitable for complex and changeable network environment.

Key words: concept drift, data stream classification, sliding window, OS-ELM, oversampling