## 5 Conclusions  
In this world of advancements, most industrial sensor-based applications generate continuous non-stationary data streams which have inbuilt issues of concept drift and class imbalance. Due to the change in concept, the performance of the output predicting model decreases. To counter this issue, a variety of drift detection methods are proposed to detect the concept drift and adapt the model to a new concept. These methods struggle when the data have a multi-class imbalance issue. To examine how these drift detection methods perform on multi-class data streams, this work compares 10 state-of-the-art drift detection methods on synthetic multi-class balanced and imbalanced data streams. We generated these data streams with sudden, gradual, and incremental drifts. The data stream also faces the issue of class imbalance, so two copies of each data stream were generated with the same type of concept drift. One with class imbalance (imbalanced data stream) and the other without class imbalance (balanced stream). The performance was evaluated in terms of the F1 score. The other performance measures were also used for sudden and gradual drifts such as false alarms and delays in drift detection.  
This experimental study accomplishes the following results:  
* Every drift type (sudden, gradual, or incremental) has a different effect on the performance of the base classifier.
* None of the compared methods showed consistent performance on different data streams.
* Most of the drift detection methods were affected by the class imbalance issue.
* Our results showed that DDM produced the best overall ranking among the compared methods but significantly different from STEPD, HDMM_W, and EWMAC only.
* DDM also produced great results on data streams with class imbalance.
* The STEP_D, EWMAC, and HDDM_W produced poor results on both balanced and imbalanced data streams.
* Our results also showed that the drift detection method may cause a decrease in base classifier performance in case it generates a false alarm.  
Based on the results of this study, we can conclude that the existing drift detection methods are not only lacking in the detection of multiple drifts, but they also struggle when data face multi-class imbalance issues. Therefore, there is a need to design adaptive drift detection methods that can quickly learn the new concepts and update themselves accordingly without generating false alarms. This is a goal for our future work. We are also planning to test more existing drift detection methods as well as the latest methods on numerous multi-class data streams with dynamic class imbalance ratio.  
Conceptualization, A.S.P. and J.J.; methodology, H.M.G.; software, A.S.P.; validation, H.M.G., A.R.G. and M.A.H.; formal analysis, J.J.; investigation, A.S.P.; resources, A.S.P.; data curation, H.M.G.; writing--original draft preparation, A.S.P.; writing--review and editing, J.J. and A.R.G.; visualization, A.S.P.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UTP URIF (grant number: 015 LB0-053).  
Not applicable.  
The authors acknowledge the Center for Research in Data Science and High-Performance Cloud Computing Centre (HPC3), Universiti Teknologi PETRONAS for their support to complete this research study.  
The authors declare no conflict of interest.