## 4 Experiment Evaluation
### Experimental Analysis
#### 4.3.1 Base Classifiers Performance Analysis
From Figure 7a,b, it can be observed that the performance of VFDDM remains consistently high, with a faster response to abrupt drift and minimal performance loss at most drift points, highlighting the advantage of VFDDM in detecting abrupt drifts swiftly. In Figure 7c,d, the accuracy curve of VFDDM also ranks prominently. Although in the LED dataset, VFDDMs' performance falls short compared to RDDM and HDDM_A, it outperforms these algorithms in other datasets. Figure 7: The accuracy trend of NB using different drift detectors on the synthetic datasets. Figure 8 illustrates the accuracy trends of HT using different drift detectors on four synthetic datasets. In Sine (Figure 8a), it can be observed that VFDDM maintains consistently high classification accuracy, with minimal performance loss at drift points compared to other drift detectors. In LED (Figure 8c), its performance is similar to that in Figure 7c. In the Mixed dataset, drift detectors with conservative detection tendencies perform better, such as FHDDM and WMDDM. FHDDM, which does not use a weighting strategy, outperforms MWDDM with a strong weighting strategy. WMDDM, based on the McDiarmid inequality, uses a Sigmoid function to dynamically generate instance weights based on the current classification accuracy, achieving adaptive weighting. This approach results in a higher overall accuracy compared to MDDM. This phenomenon in HT demonstrates its inherent adaptability in handling data stream classification tasks. The stable accuracy performance of most drift detectors in the Mixed dataset further confirms this observation. Combining observations from Figures 7 and 8, HT clearly outperforms NB in overall accuracy, highlighting HT's advantage in data stream classification tasks. In vertical comparisons, VFDDM_H generally outperforms FHDDM and MWDDM in most cases; similarly, VFDDM_M generally outperforms MDDM and WMDDM in most cases, validating the effectiveness of the proposed VFDDM. This framework can enhance detection performance for original drift detection methods in most scenarios. In summary, VFDDM demonstrates excellent classification accuracy in most cases, with minimal performance loss at most drift points. While it may not achieve optimal performance in certain scenarios,  
Figure 8: The accuracy trend of HT using different drift detectors on the synthetic datasets. its performance remains consistently high across all scenarios, indicating its significant overall advantage. This underscores our VFDDM's strong generality and competitiveness. Table 3 presents the time consumption results of the proposed algorithm compared to other benchmark algorithms. From Table 3, it can be observed that VFDDMs exhibit good time consumption performance in both NB and HT, comparable to FHDDM, BDDM, HDDM_A, and HDDM_W. Theoretically, for any given moment, FHDDM requires only cumulative operations to obtain the window mean, resulting in a time complexity of \(O(1)\), which is independent of the window size. Therefore, FHDDM consistently exhibits optimal time consumption both theoretically and experimentally. VFDDM_H employs a weighting strategy, which requires a traversal of the window each time it slides, resulting in a time complexity of \(O(n)\). Consequently, VFDDM_H has higher time consumption compared to FHDDM. VFDDM_M exhibits similar characteristics. In contrast, VFDDM_K, which uses the Kolmogorov test in high-variance data streams, does not rely on a weighting strategy and can also use cumulative calculations to obtain the mean. Thus, VFDDM_K achieves the same theoretical time complexity as FHDDM, which is \(O(1)\), in high-variance data stream environments. This explains why VFDDM_K performs better in terms of time consumption compared to VFDDM_H and VFDDM_M. However, the time consumption differences among the three variant algorithms are relatively low. MWDDM uses a multi-stage weighting approach, resulting in a theoretical time complexity of \(O(n)\) for each window slide.