## 4 Implementation and Experimental Results
### The Experimental Results  
The model performance evaluation depended on the Test then Train method because the data are a stream, not a batch file. In this method, each instance that arrives in the current sliding window will be considered a test instance and used to evaluate the model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature’s** & **Correlation** & **Correlation** & **Feature’s** & **Correlation** & **Correlation** \\
**Name** & **A** & **B** & **Name** & **A** & **B** \\ \hline ch1 & 0.837 & \(-\)0.016 & ch13 & 0.833 & \(-\)0.016 \\ ch2 & 0.839 & \(-\)0.013 & ch14 & 0.862 & \(-\)0  
Then, the instance will be used to train the model. The mean accuracy will be calculated for every 100 instances depending on testing performance. So, in this method, all data elements will be used for testing and training to avoid bias such as the cross-validation in batch data mining. Applying the proposed model using the CHB-MIT dataset led to some significant results that confirm its advantage. Considering the normal signal is a negative class and the seizure is a positive class, Table 3 shows that the proposed model had the best True Positive Rate TPR compared to Random Forest, J48 Decision Tree, Reduced Error Pruning Tree (REPTree), AdaBoost, and Bayes Network classifier. In addition to the high value of True Negative Rate TNR, ARFC also has a low False Positive Rate, which is most important. The implementation detected 37 changes in the extracted EEG data stream. The size of the ADWIN window has been changed accordingly; as we can see in Figure 4, the average window size was 1251, while the maximum size was 10,240. The large size of ADWIN points to more stability in the distribution of data stream and vice versa. Table 4 showed that the proposed model has the best performance in comparison to five stream classifiers: Rule-based, VFDT, Random VFDT, OzaBagging, and Adaptive-NN. The comparison is included in Table 4 using five metrics: accuracy, Kappa, precision, Recall, and F1 score. Tracking of the proposed model accuracy during the classification of the EEG data stream is illustrated in Figure 5. The stability of its accuracy can be noticed; the accuracy of seizures also tends to increase. Figure 5 illustrates that the obvious change in ADWIN's size had an effect on the True Positive Rate (i.e., seizures class), as we can see in the periods (20,991-37,151) and (58,175-70,079). In both Figures 4 and 5, the index of the distribution's change refers to the position of the instance where the change in distribution was declared by ADWIN. \begin{table}
\begin{tabular}{c c c c c} \hline
**Technique** & **Accuracy** & **Kappa** & **Precision** & **Recall** & **F1 Score** \\ \hline VFDT & 0.9894 & 0.6458 & 0.5896 & 0.727 & 0.6512 \\ Adaptive-NN & 0.9908 & 0.6361 & 0.6911 & 0.5972 & 0.6407 \\ OzaBagging & 0.9917 & 0.634 & 0.6951 & 0.5905 & 0.6386 \\ ARFC & **0.9959** & **0.8707** & **0.8425** & **0.9048** & **0.8726** \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance between ARFC and three stream classifiers using five metrics. \begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **True Negative Rate** & **True Positive Rate** & **False Positive Rate** \\ \hline Random Forest & 0.989 & 0.007 & 0.0113 \\ J48 & 0.999 & 0.092 & 0.000 \\ REPTree & 0.999 & 0.002 & 0.0002 \\ AdaBoost & 0.996 & 0.129 & 0.013 \\ Bayes Network & 0.817 & 0.709 & 0.19 \\ ARFC & 0.9983 & **0.7852** & 0.0017 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the accuracy between ARFC and five batch classifiers. Another observation that has been noticed from the performance of the proposed model is its robustness of it against the missing values in the class label, which is one of the most difficult challenges for data stream classification tasks. Table 5 shows a comparison with the other five stream classifiers using two versions of the CHB-MIT EEG dataset. In the first one, we removed 1% from the values of the class label, while in the second one, we removed 3% from these values.