## 4 Experiment Evaluation
### Experimental Analysis
#### 4.3.1 Base Classifiers Performance Analysis
Similarly, in the Spam dataset, it can be observed that the accuracy performance of VFDDMs is comparable to HDDM_W, while HDDM_W reports a higher number of drift warnings than VFDDMs. As previously analyzed in drift detection performance, HDDM_W has a relatively high false positive rate, which is further evidenced by its performance in NB-Spam, emphasizing the advantage of VFDDMs in having a low false positive rate. In the HT classifier, the performance contrast of various drift detectors in the Spam dataset is most pronounced. The performance of VFDDMs is superior to MWDDM, with lower average warning counts, indicating higher precision in drift detection compared to \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Datasets**} & \multicolumn{2}{c}{**Covertype**} & \multicolumn{2}{c}{**Electricity**} & \multicolumn{2}{c}{**PokerHand**} & \multicolumn{2}{c}{**Spam**} \\ \cline{2-10}  & **Metrics** & **Acc** & **Alert** & **Acc** & **Alert** & **Acc** & **Alert** & **Acc** & **Alert** \\ \hline \multirow{10}{*}{NB} & FHDDM & 83.83 & 0.49 & 82.87 & 0.37 & 76.72 & 0.37 & 88.77 & 0.02 \\  & MWDDM & 85.16 & 0.78 & 84.76 & 0.55 & 77.47 & 0.51 & **89.59** & **0.11** \\  & MDDM & 84.04 & 0.55 & 83.19 & 0.41 & 76.90 & 0.39 & 88.78 & 0.02 \\  & WMDDM & 83.92 & 0.50 & 82.89 & 0.39 & 76.70 & 0.38 & 88.96 & 0.04 \\  & BDDM & 83.76 & 0.62 & 83.64 & 0.46 & 75.37 & 0.28 & 88.89 & 0.02 \\  & RDDM & 86.16 & 0.94 & 85.09 & 0.72 & 77.27 & 0.62 & 88.79 & 0.40 \\  & HDDM\_A & 86.59 & 1.13 & **85.58** & **0.93** & 77.08 & 0.63 & 88.87 & 0.19 \\  & HDDM\_W & 85.66 & 0.82 & 85.13 & 0.58 & **77.76** & **0.56** & 89.12 & 0.11 \\  & DDM & **86.61** & **1.59** & 82.57 & 0.63 & 65.84 & 0.10 & 84.09 & 0.09 \\  & VFDDM\_K & 84.37 & 0.60 & 83.31 & 0.44 & 76.98 & 0.40 & 89.12 & 0.09 \\  & VFDDM\_H & 84.42 & 0.62 & 83.41 & 0.46 & 77.01 & 0.40 & 89.12 & 0.09 \\  & VFDDM\_M & 84.41 & 0.61 & 83.40 & 0.46 & 76.97 & 0.40 & 89.12 & 0.09 \\ \hline \multirow{10}{*}{HT} & FHDDM & 83.98 & 0.47 & 86.20 & 0.27 & 76.88 & 0.35 & 90.46 & 0.04 \\  & MWDDM & 85.21 & 0.75 & 86.37 & 0.47 & 77.50 & 0.49 & 90.66 & 0.13 \\  & MDDM & 84.08 & 0.53 & 86.22 & 0.33 & 77.05 & 0.37 & 90.54 & 0.04 \\  & WMDDM & 84.07 & 0.49 & 86.56 & 0.29 & 76.86 & 0.36 & 90.62 & 0.06 \\  & BDDM & 83.93 & 0.59 & 86.56 & 0.38 & 75.48 & 0.27 & 90.74 & 0.04 \\  & RDDM & 85.81 & 0.92 & **86.94** & **0.63** & 77.40 & 0.61 & 90.89 & 0.38 \\  & HDDM\_A & 86.52 & 1.27 & 86.94 & 0.93 & 77.09 & 0.62 & 90.78 & 0.21 \\  & HDDM\_W & 85.43 & 0.81 & 86.81 & 0.52 & **77.75** & **0.53** & 90.51 & 0.17 \\  & DDM & **87.22** & **1.48** & 86.71 & 0.74 & 73.68 & 0.25 & **90.89** & **0.85** \\  & VFDDM\_K & 84.35 & 0.59 & 86.28 & 0.37 & 77.07 & 0.38 & 90.85 & 0.09 \\  & VFDDM\_H & 84.42 & 0.59 & 86.29 & 0.38 & 77.15 & 0.39 & 90.85 & 0.09 \\  & VFDDM\_M & 84.39 & 0.59 & 86.29 & 0.38 & 77.13 & 0.39 & 90.85 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy and average number of drift alerts of drift detectors for base classifiers on the real datasets (The best results are highlighted in bold). MWDDM. Similar observations are noted for HDDM_A and HDDM_W. Although DDM performs best in this dataset, achieving a slight improvement of 0.04 compared to VFDDMs, the average warning counts reveal that DDM has a count of 0.85 while VFDDMs have a count of 0.09, and both have similar accuracy. Therefore, it can be inferred that DDM exhibits a higher false positive rate in its operation, further demonstrating the advantage of VFDDMs. In conclusion, based on the above analysis, we believe that the VFDDM demonstrates outstanding performance in drift detection on real datasets, with high precision in detecting real concept drifts.