## 4 Experimental Design
### Parameter Sensitivity Experiments  
Parameter sensitivity experiments are able to demonstrate the parameter sensitivity of the proposed HSDW-MI algorithm. In this subsection, we conducted experiments on three parameters, which are chunk size \(D\), number of base classifiers \(N\), and ensemble update threshold \(\theta\). Parameter sensitivity experiments were conducted on 10 synthetic data streams, and the results obtained are the average values. Table 3 shows the parameter sensitivity experiments on five stationary data streams, and Table 4 shows the parameter sensitivity experiments on five variable data streams. As shown in Tables 3 and 4, the performance of the algorithm improves as the chunk size increases and works best when \(D\) = 2000. This indicates that as the chunk size increases, minority classes in the chunk obtain more instances, allowing the classifier to better fit the current chunk. However, the performance of the algorithm is slightly lower when \(D\) = 2500 and \(D\) = 3000 than \(D\) = 2000, which is because the size of the storage pool is dependent on the chunk size. When concept drift occurs, the storage pool stores instances on the current drifted chunk and the larger the chunk the more drifted instances are stored. This will lead to more drift instances being sampled in the hybrid sampling phase, which further affects the final classification results. The experiment on the variation of the number of base classifiers shows that increasing the number of base classifiers helps the algorithm to obtain better performance. Similarly, as the updating threshold \(\theta\) of the ensemble increases, the algorithm obtains a small improvement in its effectiveness. That is because setting a higher update threshold helps the ensemble to remove unqualified classifiers in time and keep the classifiers to adapt to the drift after the concept drift occurs. In conclusion, variations \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Size of Chunk \(D\)**} & \multicolumn{6}{c}{**Number of Base Classifiers \(N\)**} & \multicolumn{6}{c}{**Updating Threshold of Ensemble \(\theta\)**} \\ \cline{2-13}  & **500** & **1000** & **1500** & **2000** & **2500** & **3000** & **5** & **10** & **15** & **20** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** \\ \hline mR & 90.09 & 91.24 & 92.06 & 92.33 & 92.26 & 92.20 & 90.83 & 91.77 & 92.07 & 92.33 & 91.45 & 91.79 & 92.08 & 92.33 & 92.17 \\ Gm & 89.88 & 90.97 & 91.69 & 91.81 & 91.78 & 91.62 & 90.19 & 90.90 & 91.48 & 91.81 & 91.00 & 91.22 & 91.60 & 91.81 & 91.78 \\ mP & 87.05 & 88.50 & 89.15 & 89.70 & 89.38 & 89.15 & 87.84 & 88.91 & 89.42 & 89.70 & 88.58 & 88.94 & 89.24 & 89.70 & 89.21 \\ mF1 & 87.88 & 89.21 & 90.33 & 90.38 & 90.30 & 90.21 & 88.91 & 89.87 & 90.31 & 90.38 & 89.46 & 89.71 & 90.27 & 90.38 & 90.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of parameter sensitivity experiments on variable streams. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Size of Chunk \(D\)**} & \multicolumn{6}{c}{**Number of Base Classifiers \(N\)**} & \multicolumn{6}{c}{**Updating Threshold of Ensemble \(\theta\)**} \\ \cline{2-13}  & **500** & **1000** & **1500** & **2000** & **2500** & **3000** & **5** & **10** & **15** & **20** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** \\ \hline mR & 89.90 & 92.74 & 93.94 & 94.15 & 93.98 & 93.96 & 91.98 & 94.09 & 93.93 & 94.15 & 92.87 & 93.62 & 93.74 & 94.15 & 93.90 \\ Gm & 89.74 & 92.66 & 93.89 & 94.05 & 93.89 & 93.93 & 91.93 & 93.04 & 93.67 & 94.05 & 92.81 & 93.56 & 93.71 & 94.05 & 93.83 \\ mP & 86.21 & 89.26 & 90.24 & 91.02 & 90.42 & 90.43 & 89.60 & 90.26 & 90.78 & 91.02 & 90.29 & 90.82 & 90.96 & 91.02 & 90.87 \\ mF1 & 87.65 & 90.75 & 91.97 & 92.27 & 92.14 & 92.03 & 90.08 & 90.10 & 91.94 & 92.27 & 91.01 & 91.63 & 91.91 & 92.27 & 92.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of parameter sensitivity experiments on stationary streams.