## 4 Results and Discussion
### Balanced and Imbalanced Data Stream with Sudden Drift  
Results of the experiments in terms of F1-score on balanced and imbalanced data streams are presented in Figure 3. It can be observed from the results that the base classifier has produced better results on balanced data streams compared to the imbalanced data stream. Another notable aspect to observe is that the performance of the base classifier without the drift detection method (NoCD) produced far better results than using drift detection methods except GMA. In other words, instead of performance improvement, the performance of the base classifier has been decreased using most of the compared drift detection methods. Figure 4 shows the actual effect of each drift detection method on base classifier performance in terms of percentage increase or decrease. On the y-axis if the value is <0, it means the performance is decreased, and >0 shows the performance improvement, whereas 0 shows no effect. The performance of HT as a base classifier on balanced and imbalanced data streams was decreased using most of the drift detection methods. The performance of the base classifier with GMA remained unchanged, i.e., neither increased nor decreased on balanced as well as imbalanced data streams. Whereas, with DDM and HDDM_A, the performance was slightly improved on the imbalanced data stream. These results raise the question of why the performance of the base classifier is decreasing using drift detection methods instead of improving. It has been analyzed from another perspective, i.e., the number of false drift alarms. A false alarm takes place when the detector signals a change when there were none. So, when the drift detection method generates the false alarm, it triggers mechanism to reset the base classifier. As a result, the base classifier forgets the previously learned knowledge. In case there is no drift in the data streams and the concept is still the same and the drift detection method generates  
Figure 3: F1 score on balanced and imbalanced data streams with sudden drift. a false alarm (considering that there is a drift), the base classifier will be reset and starts learning from the new concept but in real concept is still the same. This situation causes performance degradation. Therefore, any drift detection method which generates a high number of false alarms causes a decrease in performance. Figure 5 shows the number of false alarms generated by each drift detection method on the balanced and imbalanced data streams. It can be observed that HDDM_W, STEPD, and EWMAC generate a high number of false alarms, and these are the methods that cause more damage to the base classifier's performance. On an imbalanced data stream, both DDM and HDDM_A methods correctly detected actual drift. They did not generate many false alarms, and as a result, they slightly improved the performance of the base classifier. The performance of drift detection methods was also measured on a few other metrics such as True Detection, False Alarm, and Delay in Detection, which are described in [23]. Figure 4: Effect of drift detection methods on base classifier’s performance: (a) sudden drift without class imbalance; (b) sudden drift with class imbalance. Figure 5: Number of false drifts detected by each method on balanced and imbalanced data streams. The results are presented in Table 4. As can be seen in Table 4, the value 0 in column 'Drift Detected' against GMA highlights that it did not identify any of the drift out of three drifts available in the data stream; hence, it missed all 3 drifts. The distance (T) represents the distance in time stamp from the actual drift location to the drift detection location, whereas the distance (I) is the distance (in a number of instances) from the first instance of the new concept to the instance where drift is detected by the drift detection method. Any drift detection method which produces poor results on any of the metrics in Table 4 will affect the performance of the base classifier.