Concept drift is a difficult problem in the field of data stream learning, while the class imbalance problem existing in the data stream can seriously affect the classification performance of the algorithm. To address the joint problem of concept drift and class imbalance, this paper proposed an incremental weighted ensemble for imbalance learning(IWEIL) method for classifying unbalanced data streams by introducing an online update mechanism on the method based on the integration of data chunks, combined with the resampling and forgetting mechanism. The IWEIL method utilized a variable-size window-based forgetting mechanism to determine the classification performance of base classifiers for a number of recent instances within the window, and calculated the weights of the base classifiers. It updated each base classifier and its weight in IWEIL online as a new instance reached every time. The IWEIL used an improved adaptive nearest-neighbor SMOTE method to generate new minority class instances that conformed to the new concept to solve the class imbalance problem in the data stream. The experimental results show that compared with the DWMIL algorithm, IWEIL method improves the G-mean and recall on the synthesized HyperPlane dataset by 5.77% and 6.28% respectively, and the two metrics on the real-world Electricity dataset by 3.25% and 6.47% respectively. Finally, IWEIL has performed well in the Android app detection problem.

[2023-10-12] Accepted Paper /doc/2023.08.0330/v1

[2024-03-05] Printed Article /doc/2023.08.0330/v2