Active Learning Method for Imbalanced Concept Drift Data Stream

Data stream classification researchs how to provide more reliable data-driven prediction models in open and dynamic environment. The key is how to detect and adapt to concept drift from continuously changing data stream that arrive in real-time. Currently, in order to detect concept drift and update classification models, data stream classification methods usually assume that the labels of all samples are known, which is unrealistic in real scenarios. Additionally, real data stream may exhibit a high and constantly changing class imbalance ratios, further increasing the complexity of the data stream classification task. In this paper, we propose an active learning method for imbalanced concept drift data stream (ALM-ICDDS). Firstly, we define a sample prediction certainty measure based on multiple prediction probabilities and propose an adaptive adjustment method for the margin threshold matrix, which makes the label query strategy suitable for imbalanced data stream with a number of categories. Then, we propose a sample replacement strategy based on memory strength, which saves the samples that are difficult-to-distinguish, minority class and represent the current data distribution in the memory window, and improves the classification performance of new base classifier. Finally, we define the importance evaluation and update method of base classifier based on classification accuracy, which realizes the ensemble classifier update after drift. Comparative experiments on seven synthetic data streams and three real data streams show that the active learning method for imbalance concept drift data stream is better than six concept drift data stream learning methods in classification performance. © 2024 Science Press. All rights reserved.