## 5 Conclusions  
Concept drift significantly impacts decision-making efficiency, making it crucial to capture concept drift quickly and accurately. In current drift detection research, there are few approaches that dynamically adjust drift detection methods based on the characteristics of the data stream. Our research utilizes the variance of data streams to dynamically adjust drift detection strategies, thereby providing personalized drift detection strategies for different data streams. Figure 11: The overall ranking of different drift detectors based on four key metrics: accuracy, CPU time, detection delay, and false positives. Firstly, to estimate the variance in data streams under small window conditions, we propose a rapid variance estimation strategy, using the minimum Bernstein bound as the condition for variance sampling. Secondly, we introduce a variance feedback strategy that adjusts the weighting and mean calculation methods to provide targeted detection strategies for each data stream, improving detection speed and classifier accuracy. Lastly, based on these two strategies, we design a variance feedback-based drift detection method, VFDDM, and implement three drift detectors based on different testing methods: VFDDM_H, VFDDM_M, and VFDDM_K. Experiments show that base classifiers based on VFDDMs achieve optimal overall classification accuracy. The three drift detectors based on VFDDM rank in the top three for accuracy among all compared algorithms. For drift detectors using the same statistical testing methods, there is a significant improvement in base classifier accuracy. The average ranking improvement for [PERSON]'s test ranges from 3 to 3.5, and for [PERSON]'s test, it ranges from 1.12 to 2.25. For weighted strategy drift detection algorithms, the improvement ranges from a minimum of 1.12 to a maximum of 4.25. These results thoroughly demonstrate the effectiveness of the proposed variance feedback strategy. This strategy allows for the setting of appropriate drift detection strategies for each data stream to some extent. In drift detection performance comparisons, VFDDM exhibits a low false positive rate while maintaining low detection delay, balancing these two metrics effectively without excessively sacrificing one for the other. Based on the accuracy performance of base classifiers using VFDDMs, we can conclude that balancing false positives and detection delay is beneficial for improving base classifier accuracy. This is precisely the advantage of the proposed methods. In a comprehensive ranking analysis based on all evaluation metrics (accuracy, runtime, false positive rate, and detection delay), VFDDMs outperform other comparison algorithms, indicating the effectiveness of the VFDDM algorithm. This also demonstrates the effectiveness of our approach, which characterizes data stream features using variance and dynamically adjusts drift detection methods based on estimated variance. Future work will focus on three aspects: Firstly, we will explore more statistical test methods and investigate their potential for concept drift detection, integrating them into the VFDDM. Secondly, we plan to incorporate a stacked window strategy into the VFDDM to further improve drift detection efficiency. Lastly, we will consider the feasibility of implementing a dynamic window in the VFDDM. [PERSON] completed the main work, the coding of the model, experiments and the writing of the main paper; [PERSON] reviewed the paper, and provided guidance on experiments and funding support; [PERSON] completed some experiments and the production of experimental diagrams. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (62062004), the Natural Science Foundation of Ningxia Province (2022 AAC03279), and the Central Universities Foundation of North Minzu University (2021 KJCX10). Not applicable.