## 4 Experiment Evaluation
### Experimental Analysis  
#### 4.3.1 Base Classifiers Performance Analysis  
This section provides a detailed analysis of the classification performance of various drift detection algorithms, including the algorithms proposed in this paper, in terms of both accuracy and time consumption. Table 2 presents the classification accuracy performance of NB and HT under different drift detectors on synthetic datasets. From Table 2, it can be observed that the classification models using VFDDMs demonstrate good classification accuracy on artificial datasets. In overall accuracy comparison, VFDDMs achieve an average accuracy ranking among the top three, indicating the excellent classification performance of VFDDMs. In the comparison among the same base classifiers, VFDDMs also achieved optimal classification performance. In NB, the average rankings of VFDDM_K, VFDDM_H, and VFDDM_M were 2.5, 3.25, and 3.75, respectively. Additionally, the best-performing algorithms besides VFDDMs were MDDM and WMDDM (ranked 4.75 and 5.5). Furthermore, the lowest ranking of the proposed algorithms on NB was 7 (see the performance of VFDDM_M on the Mixed dataset), while the rankings of the other situations for the three algorithms were all in the top 50%. Similarly, in HT, all three variants of VFDDM showed the best average performance, followed by MDDM and MWDDM. Moreover, their accuracy rankings were all in the top 50% among all artificial datasets. This analysis demonstrates that the proposed algorithm exhibits strong adaptability and can provide excellent classification performance for different base classifiers on various datasets. Furthermore, this paper will conduct a horizontal comparison of drift detection methods using the same statistical test method and demonstrate the advantages of the proposed algorithm. In drift detection methods using the Hoeffding test, FHDDM treats window instances following a binomial distribution without distinguishing the importance of instances within the window. Therefore, its performance is not as good as the MWDDM with a weighted strategy, with a difference in overall ranking of 0.5. On the other hand, the proposed Hoeffding test drift detector VFDDM_H significantly outperforms MWDDM in terms of accuracy. This is because the VFDDM adopts a dynamic weighting strategy, where the importance difference of instances is influenced by the overall variance of the data stream. Thus, its adaptability is relatively stronger compared to MWDDM. Additionally, MWDDM employs a conservative fixed weighting strategy in the warning phase, leading to an increase in the amount of retraining, which in turn affects the accuracy performance of the base classifier. In the comparison of drift detection methods based on the McDiarmid test, VFDDM_M and MDDM both used the same weighting strategy in the experiment. However, VFDDM_M's weighting conservatism can be self-adjusted according to the data stream environment, resulting in superior accuracy performance. Additionally, VFDDM_M also outperforms the adaptive adjustment strategy-based WMDDM. This is because WMDDM's adaptive adjustment strategy involves adjusting decay parameters after the warning phase to accelerate detection speed without tracking the characteristics of the data stream itself. Additionally, the Kolmogorov test can only handle binomially distributed data instances in drift detection due to the Kolmogorov inequality constraints. Weighting cannot be applied in this part, thus failing to reflect the differences in importance among instances. Thus, only VFDDM_K with semi-stage weighting outperforms the fully weighted MWDDM, MDDM, and WMDDM algorithms, further proving the effectiveness of our proposed strategy. Figures 7 and 8 illustrate the accuracy trends of the tested algorithms on NB and HT, respectively. The dashed vertical lines in the graphs represent drift points, with 4 drift