# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 5 Conclusions  
This study mainly introduces an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID) to solve the joint challenges of concept drift and class imbalance faced when the label budget is limited. First, by introducing a dynamic label budget strategy, the learning rate of instances during drift is increased to achieve fast recovery after drift. Secondly, by designing an uncertainty label request strategy based on a dual-marginal dynamic threshold matrix to select the most informative instances in the data stream, save the label budget and focus on difficult instances and \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Fixed\_Mix\_SIG**} & \multicolumn{4}{c}{**Var\_Mix\_SIG**} & \multicolumn{4}{c}{**Connect-4**} \\ \cline{2-13}  & **V0** & **V1** & **V2** & **V3** & **V4** & **V0** & **V1** & **V2** & **V3** & **V4** & **V0** & **V1** & **V2** & **V3** & **V4** \\ \hline Recall & **91.91** & 91.59 & 91.40 & 91.30 & 87.97 & **91.85** & 90.97 & 91.58 & 89.45 & 89.92 & 57.66 & 54.74 & 52.58 & 54.02 & 53.06 \\ Accuracy & **93.74** & 92.80 & 93.36 & 92.68 & 89.91 & **95.63** & 94.68 & 95.56 & 94.05 & 94.63 & **80.70** & 76.31 & 75.01 & 73.25 & 75.17 \\ G-mean & **91.35** & 91.03 & 91.25 & 90.57 & 86.87 & **89.76** & 89.60 & 89.56 & 87.34 & 89.07 & **47.44** & 44.96 & 42.84 & 46.70 & 44.02 \\ F1-score & **92.17** & 91.70 & 91.68 & 90.81 & 87.89 & **93.12** & 92.49 & 92.83 & 91.07 & 90.35 & **59.90** & 56.22 & 54.41 & 54.32 & 54.47 \\ Kappa & **91.53** & 90.62 & 91.20 & 90.03 & 86.18 & **92.83** & 91.91 & 92.69 & 90.04 & 91.26 & **51.58** & 45.02 & 41.00 & 40.14 & 40.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of the AdaAL-MID ablation study. Figure 11: Recall curves of the AdaAL-MID ablation study: (**a**) Fixed_Mix_SIG; (**b**) Var_Mix_SIG; (**c**) connect-4. minority class instances. Finally, through an adaptive sampling strategy that combines class proportions and classification performance, greater attention is given to minority class instances, and a weighted ensemble strategy is used to effectively improve the prediction accuracy of the ensemble. Algorithm comparison experimental results show that the AdaAL-MID is better than advanced active learning algorithms in handling concept drift and imbalance ratio changes. In addition, ablation studies also demonstrated the effectiveness of each strategy in the AdaAL-MID. Currently, the performance of the AdaAL-MID under specific types of concept drift has been studied. In future work, we plan to explore more complex forms of concept drift, such as recurring drift. Additionally, a core aspect of active learning is the effective selection of the most valuable samples for labeling. In large-scale applications, the cost of manually labeling data can be very high. As the scale of data streams continues to grow, efficiently handling large-scale data streams becomes an urgent task. Future research could explore strategies like parallel computing and distributed processing to ensure efficient and scalable data mining techniques in the context of large-scale data streams. M.H.Writing--review and editing, Validation, Supervision, and Conceptualization. C.L. : Writing--original draft, Validation, Methodology, Conceptualization, and Writing--review and editing. F.M. : Writing--review and editing, Data curation, and Investigation. F.H. : Writing--review and editing, Visualization, and Software. R.Z. : Writing--review and editing, Software, and Resources. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Nature Science Foundation of China (No. 62062004), the Ningxia Natural Science Foundation Project (No. 2022 AAC03279) and the Central Universities Foundation of North Minzu University (No. 2021 KJCX10). Not applicable. The data presented in this study are available upon request from the corresponding author.