# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 2 Related Work  
### Supervised Learning Methods for Multiclass Imbalanced Data Streams  
Supervised learning methods typically assume that instance labels in data streams are immediately available. The current supervised learning methods addressing the joint challenges of concept drift and multiclass imbalance in data streams can be broadly divided into block-based batch processing methods and instance-by-instance online learning methods [14]. Block-based algorithms partition instances in the data stream into chunks. When a chunk is filled with instances, the model learns and updates chunk by chunk to adapt to potential concept changes. These methods typically apply resampling techniques to address imbalanced distributions within data chunks, enhancing the algorithm's ability to handle imbalanced data streams. The meta-cognitive online sequential extreme learning machine (MOS-ELM) [15] algorithm was the first to focus on the joint problem of concept drift and multiclass imbalance. It combines data sampling and cost-sensitive weighting ideas and introduces an adaptive window method to handle concept drift in data streams. However, it cannot determine the appropriate chunk size and cannot respond to concept drift within individual chunks. The selection-based resampling ensemble (SRE) [16] introduced a selective resampling mechanism by reusing minority class instances from past chunks to balance the class distribution in the current chunk. It then avoids absorbing drifted data by assessing the similarity between minority class instances from past chunks and those in the current chunk. The weighted ensemble with one-class classification and over-sampling and instance selection (WECOI) [17] combines k-nearest neighbors with minority class instances in the current chunk to generate new synthetic minority class instances to balance the class distribution, and it uses instance selection techniques to replace old instances with new ones for updating data chunks. The above algorithms all use a fixed chunk size, which may not be suitable for cases in which the data stream changes frequently and the instance distribution is unstable. Therefore, the adaptive chunk-based dynamic weighted majority (ACDWM) [18] proposes a method for adaptively selecting chunk sizes through statistical hypothesis testing and dynamically weighting classifiers in an ensemble based on their performances on the current data chunk, addressing the limitations of a fixed chunk size. In online learning methods, one instance is processed at a time, which can also address the difficulty in determining the block size in block-based methods and is more sensitive to concept changes. In recent years, an increasing number of researchers have focused on online learning from data streams, with most studies adopting the concept of online bagging (OB) [19], utilizing Poisson distribution \(k\sim Poisson(\lambda)\) for online resampling. For example, LB and MOOB manipulate the parameter \(\lambda\) to determine the sampling rate of instances, thereby obtaining approximately balanced training data and training balanced classifiers. The adaptive random forests with resampling (ARFre) [20] extends and inherits the concept drift detection mechanism of ARF [21]. Compared with the original ARF, ARFre changes the calculation method of \(\lambda\), reducing the chance of majority class instances being learned by ARF trees, while minority classes have a greater chance of learning, thus improving the ability to handle imbalanced data streams. Streaming random patches (SRP) [22] combines random subspaces and online bagging to achieve high precision by training models on random subsets of features and instances. By training a background model and replacing the main model when concept drift is detected to mitigate the impact of concept changes on the main model, but the mechanism of the background model may consume more computing resources.