# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 2 Related Work
### Active Learning Methods for Data Streams  
Active learning methods in data streams can selectively label a small number of instances with the highest information or importance in real time to construct predictive models, effectively addressing limited label budgets. Among these methods, the most crucial aspect is the label request strategy, which determines which instances in the data stream should be labeled. A good label request strategy can achieve high model performance with minimal label budgets. Label request strategies are typically categorized into uncertainty-based, random, and hybrid strategies [27]. Uncertainty-based strategies select instances with the highest model prediction uncertainty (i.e., the most informative instances) using certain uncertainty measures, such as entropy, confidence, or margin [28]. Random strategies, on the other hand, randomly select instances from the data stream without any particular selection criteria and are often used to characterize the class distribution status of the current data stream. Hybrid strategies combine uncertainty-based and random strategies, and most existing active learning algorithms are based on hybrid label request strategies.  
Minority-driven online active learning (MD-OAL) [29] introduces an abstention mechanism beneficial to minority class instances, coupled with a dynamic threshold adjustment uncertainty strategy, providing an effective approach for handling both concept drift and class imbalance simultaneously. But it is only limited to addressing imbalanced problems in binary classification and cannot handle multiclass imbalances. Reinforcement onlineactive learning ensemble for drifting imbalanced data streams (ROALE-DI) [30] similarly adopts an ensemble classifier framework consisting of a stable classifier and a dynamic classifier group, where the dynamic classifier group adjusts weights using a reinforcement mechanism and proposes a margin-based uncertainty strategy. The combination of these mechanisms improves the classification performances of minority classes and addresses the concept drift issue. However, in dynamically changing data streams, it cannot handle potentially changing class imbalance ratios. For the problem of variable class imbalance, CALMID [13] provides a solution by introducing a new instance training weight formula that comprehensively considers the class imbalance ratio and prediction difficulty, effectively handling changing imbalances. Simultaneously, a variable threshold uncertainty strategy was designed, which addresses the relative roles of majority and minority classes by utilizing an asymmetric marginal threshold matrix. Recently, a network traffic classification framework (MicFoal) [31] based on online active learning for multiclass imbalance and concept drift was proposed, designing an uncertainty label request strategy based on a variable minimum confidence threshold vector to determine whether to request labels based on the features of each instance. However, it is primarily designed for specific network traffic classification tasks, its adaptability to other types of data streams may be limited, and it cannot handle concept drift.