## 4 Experiment Evaluation
### Experimental Analysis
#### 4.3.1 Base Classifiers Performance Analysis
Therefore, based on this conclusion, increasing the number of model restarts is often  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Detector** & **Sine** & **Mixed** & **LED** & **Circles** \\ \hline \multirow{9}{*}{NB} & FHDDM & 259.66 \(\pm\) 18.26 & 298.94 \(\pm\) 23.69 & **524.63 \(\pm\) 15.65** & 254.44 \(\pm\) 12.90 \\  & MWDDM & 485.22 \(\pm\) 49.33 & 528.56 \(\pm\) 15.89 & 820.81 \(\pm\) 61.20 & 460.50 \(\pm\) 18.92 \\  & MDDM & 296.00 \(\pm\) 33.54 & 341.88 \(\pm\) 28.53 & 601.09 \(\pm\) 21.79 & 293.41 \(\pm\) 35.67 \\  & WMDDM & 484.13 \(\pm\) 41.92 & 508.59 \(\pm\) 29.46 & 832.38 \(\pm\) 36.51 & 465.44 \(\pm\) 14.01 \\  & BDDM & 259.44 \(\pm\) 20.76 & 318.53 \(\pm\) 47.17 & 639.06 \(\pm\) 42.52 & 281.09 \(\pm\) 16.64 \\  & RDDM & 257.56 \(\pm\) 33.44 & **285.88 \(\pm\) 29.06** & 611.66 \(\pm\) 23.60 & 275.88 \(\pm\) 13.73 \\  & HDDM\_A & **255.75 \(\pm\) 17.41** & 301.88 \(\pm\) 18.29 & 596.53 \(\pm\) 29.43 & 293.13 \(\pm\) 27.08 \\  & HDDM\_W & 264.97 \(\pm\) 38.20 & 306.81 \(\pm\) 28.14 & 613.78 \(\pm\) 17.44 & 261.66 \(\pm\) 19.20 \\  & DDM & 270.91 \(\pm\) 16.82 & 298.63 \(\pm\) 24.22 & 605.72 \(\pm\) 22.39 & **243.38 \(\pm\) 13.15** \\  & VFDDM\_K & 267.19 \(\pm\) 5.72 & 316.22 \(\pm\) 38.12 & 567.22 \(\pm\) 26.19 & 275.88 \(\pm\) 13.73 \\  & VFDDM\_H & 282.56 \(\pm\) 18.21 & 318.03 \(\pm\) 17.00 & 577.69 \(\pm\) 22.64 & 273.13 \(\pm\) 31.27 \\  & VFDDM\_M & 281.72 \(\pm\) 9.50 & 336.41 \(\pm\) 23.43 & 592.34 \(\pm\) 14.34 & 281.38 \(\pm\) 15.72 \\ \hline \multirow{9}{*}{HT} & FHDDM & 372.84 \(\pm\) 28.72 & 463.16 \(\pm\) 12.11 & **723.31 \(\pm\) 27.20** & **368.44 \(\pm\) 19.01** \\  & MWDDM & 609.03 \(\pm\) 47.17 & 664.28 \(\pm\) 27.66 & 975.25 \(\pm\) 38.32 & 617.97 \(\pm\) 27.74 \\  & MDDM & 409.53 \(\pm\) 18.31 & 479.13 \(\pm\) 32.01 & 832.66 \(\pm\) 46.22 & 500.50 \(\pm\) 75.78 \\  & WMDDM & 593.34 \(\pm\) 27.18 & 663.94 \(\pm\) 27.89 & 973.31 \(\pm\) 41.36 & 620.38 \(\pm\) 38.44 \\  & BDDM & 406.38 \(\pm\) 48.70 & 460.63 \(\pm\) 35.50 & 818.31 \(\pm\) 57.38 & 386.72 \(\pm\) 19.77 \\  & RDDM & 431.97 \(\pm\) 60.63 & 463.78 \(\pm\) 34.48 & 842.42 \(\pm\) 65.95 & 403.75 \(\pm\) 25.55 \\  & HDDM\_A & **372.59 \(\pm\) 26.91** & **427.69 \(\pm\) 22.52** & 786.38 \(\pm\) 35.52 & 371.53 \(\pm\) 36.49 \\  & HDDM\_W & 387.78 \(\pm\) 7.82 & 455.69 \(\pm\) 24.41 & 749.69 \(\pm\) 37.15 & 371.53 \(\pm\) 36.49 \\  & DDM & 406.63 \(\pm\) 19.38 & 527.25 \(\pm\) 35.04 & 862.50 \(\pm\) 33.15 & 410.72 \(\pm\) 41.07 \\  & VFDDM\_K & 379.91 \(\pm\) 17.17 & 456.50 \(\pm\) 28.64 & 749.06 \(\pm\) 35.80 & 388.19 \(\pm\) 19.37  
beneficial for the classification accuracy of data streams with time dependency. This also explains the phenomenon where DDM and HDDM series algorithms simultaneously achieve high levels of accuracy and average warning count on real datasets. Based on our findings, relying solely on accuracy to evaluate drift detectors on real datasets is unconvincing. Therefore, when assessing the performance of drift detectors on real datasets, it is essential to consider both accuracy and the number of drift warnings. Adopting a controlled variable approach would be more convincing than solely focusing on accuracy. In other words, while DDM and HDDM series algorithms demonstrate excellent accuracy, this does not necessarily imply that they are the optimal drift detectors for real datasets. In the NB classifier, BDDM and VFDDMs report almost the same number of drift risk instances in the Covertype dataset. However, VFDDMs exhibit approximately a 0.5 advantage in accuracy performance compared to BDDM, indicating that VFDDM is stronger in drift detection capability than BDDM. The performance of both algorithms in HT-Covertype further confirms this point. In the Spam dataset, although VFDDMs and DDM report the same number of drift warnings, VFDDMs achieve significantly higher accuracy than DDM, demonstrating the advantage of VFDDMs over DDM.