# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
## 4 Drift Detector Evaluation
#### Experiment Results and Analysis  
This section presents the results of the experiments and the analyses of each drift detector, where Tables 2-4 show the results of the experiments using Sine1, Sine2 and the Mixed datasets. The results of the Sine1 and Mixed datasets are similar. For example, the method that detects drift the fastest is STEPD, followed by our proposed method, DMDDM-S, and then FHDDM, SEED and RDDM in ascending order of delay detection. In addition, all methods detect all the drifts correctly. In relation to Sine2, DMDDM-S has the lowest average delay detection followed by STEPD, FHDDM, and SEED, respectively. In relation to computational time and memory usage, it is clearly seen that SEED, RDDM and FHDDM have the highest memory usage compared to the others. This is because these methods require more memory for storing the prediction results in the sliding windows or repositories and they use more computational time due to subwindow compression or reservoir sampling procedures. In addition to obtaining first and second place in delay detection, DMDDM-S has the lowest computational time and memory usage. The main reason for this is because DMDDM-S maintains a small number of variables compared with the others, which results in less memory usage and less execution runtime to update these variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Sine1 (A)**} \\ \hline
**Classifier** & **Detector** & **Delay** & **TP** & **Time** & **Memory** & **MeanAccuracy** \\ \hline HT \& Pre & DMDDM-S & 36.375 & 4 & 1.6 & 168 & 86.631 \\ \hline \multirow{6}{*}{HT} & FHDDM & 47.375 & 4 & 7.7 & 1048 & 85.242 \\ \cline{2-6}  & DDM & 196.225 & 2 & 3.3 & 472 & 66.633 \\ \cline{2-6}  & PHTest & 238.275 & 1.2 & 2.1 & 1240 & 66.211 \\ \cline{2-6}  & STEPD & 27.05 & 4 & 6.4 & 936 & 87.047 \\ \cline{2-6}  & SEED & 58.4 & 4 & 12 & 3572.588 & 86.969 \\ \cline{2-6}  & RDDM & 93 & 4 & 2.6 & 8656 & 86.894 \\ \cline{2-6}  & EDDM & 244.525 & 0.1 & 1.6 & 144 & 83.34 \\ \hline \multirow{6}{*}{Pre} & FHDDM & 46.562 & 4 & 7.75 & 1048 & 87.177 \\ \cline{2-6}  & DDM & 154.636 & 4 & 2.545 & 472 & 86.870 \\ \cline{1-1} \cline{2-6}  & PHTest & 249.568 & 0.090 & 1.363 & 1240 & 72.199 \\ \cline{1-1} \cline{2-6}  & STEPD & 27.35 & 4 & 5.2 & 936 & 87.199 \\ \cline{1-1} \cline{2-6}  & SEED & 56.8 & 4 & 11.5 & 3593.608 & 87.098 \\ \cline{1-1} \cline{2-6}  & RDDM & 99.875 & 4 & 2.6 & 8656 & 87.075 \\ \cline{1-1} \cline{2-6}  & EDDM & 250 & 0 & 1.6 & 144 & 72.181 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of Sine1 dataset with (10% noise). Regarding false alarm, DMDDM-S has relatively high readings, due to the parameter sensitivity of DMDDM-S. For example, as shown in Figure 5a,c, increasing \(\lambda\) (100-200-300) will entail fewer false positives (as shown in Figure 5a) but might delay change or miss detection (as shown in Figure 5c), resulting in a trade-off between false positives and delay change detection. Even with this increase, DMDDM-S is one of the highest in terms of delay detection and incurs the lowest time and memory consumption.