# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
## 4 Drift Detector Evaluation
#### Experiment Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mixed (A)**} & \multicolumn{5}{c}{**Mixed (B)**} \\ \hline
**Classifier** & **Detector** & **Delay** & **TP** & **Time** & **Memory** & **MeanAccuracy** \\ \hline HT \& Pre & DMDDM-S & 36.45 & 4 & 1.2 & 168 & 83.171 \\ \hline \multirow{5}{*}{HT} & FHDDM & 48.1 & 4 & 9 & 1048 & 72.767 \\ \cline{2-6}  & DDM & 214.575 & 1.8 & 1.9 & 427 & 69.828 \\ \cline{2-6}  & PHTest & 236.025 & 1.3 & 1.6 & 1240 & 67.876 \\ \cline{2-6}  & STEPD & 28.7 & 4 & 7.6 & 936 & 83.373 \\ \cline{2-6}  & SEED & 60 & 4 & 11.1 & 3609.606 & 83.294 \\ \cline{2-6}  & RDDM & 100.125 & 4 & 3.4 & 8656 & 83.265 \\ \cline{2-6}  & EDDM & 250 & 0 & 2.1 & 144 & 57.858 \\ \hline \multirow{5}{*}{Pre} & FHDDM & 47.45 & 4 & 7.6 & 1048 & 82.125 \\ \cline{2-6}  & DDM & 220.925 & 2.1 & 1.9 & 427 & 79.316 \\ \cline{2-6}  & PHTest & 246.55 & 0.2 & 1.4 & 1240 & 76.743 \\ \cline{2-6}  & STEPD & 30.725 & 4 & 7 & 936 & 82.143 \\ \cline{2-6}  & SEED & 60 & 4 & 11 & 3641.589 & 82.065 \\ \cline{2-6}  & RDDM & 117.575 & 4 & 2.5 & 8656 & 82.006 \\ \cline{2-6}  & EDDM & 244.65 & 0.1 & 1.5 & 144 & 76.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Mixed dataset with (10% noise). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sine2 (A)**} & \multicolumn{5}{c}{**Sine2 (B)**} \\ \hline
**Classifier** & **Detector** & **Delay** & **TP** & **Time** & **Memory** & **MeanAccuracy** \\ \hline HT \& Pre & DMDDM-S & 23.75 & 4 & 1.7 & 168 & 78.294 \\ \hline \multirow{5}{*}{HT} & FHDDM & 52.125 & 4 & 9.2 & 1048 & 79.834 \\ \cline{2-6}  & DDM & 209.2 & 3.6 & 1.5 & 472 & 77.399 \\ \cline{2-6}  & PHTest & 230 & 0 & 1.9 & 1240 & 57.738 \\ \cline{2-6}  & STEPD & 33.1 & 4 & 6.4 & 936 & 79.855 \\ \cline{2-6}  & SEED & 62.4 & 4 & 11.4 & 3638.354 & 79.729 \\ \cline{2-6}  & RDDM & 134.575 & 4 & 3.8 & 8656 & 79.55 \\ \cline{2-6}  & EDDM & 250 & 0 & 2 & 144 & 57.738 \\ \hline \multirow{5}{*}{Pre} & FHDDM & 56.675 & 4 & 10.5 & 1048 & 74.843 \\ \cline{2-6}  & DDM & 246.25 & 0.3 & 2.1 & 472 & 74.269 \\ \cline{2-6}  & PHTest & 250 & 0 & 1 & 1240 & 49.882 \\ \cline{2-6}  & STEPD & 41.725 & 4 & 6.5 & 936 & 74.851 \\ \cline{2-6}  & SEED & 70.4 & 4 & 10.6 & 3688.576 & 74.813 \\ \cline{2-6}  & RDDM & 189.625 & 3.4 & 2 & 8656 & 74.623 \\ \cline{2-6}  & EDDM & 250 & 0 & 1.4 & 144 & 74.256 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Sine2 dataset with (10% noise). Furthermore, despite this trade-off, the accuracy of DMDDM-S is constant over the sensitivity of parameters, as shown in Figure 5e. As mentioned in [16], the trade-off exists with the use of the PH test; consequently, one possible solution to overcome this trade-off is to control the diversity of the disagreement method using two fading factors instead of one as used in this work. Such a solution is left for future work. Moreover, Figure 5b,d,f shows the stability of each detector in terms of accuracy and mean accuracy over the data stream, where DMDDM-S is among the highest and most stable compared to the others. Figure 5: Changing DMDDM-S threshold (**a**,**c**,**e**) and the stability of each detector’s accuracy (**b**,**d**,**f**).