## 2 Related Work  
The major goal of concept drift detection methods is to provide an efficient approach that works together with the classification model, indicating drift or novelty when there is a substantial change in data properties [27]. The model is updated in this manner, avoiding that it be influenced by the change and so boosting its predicted performance. The existing drift detection methods are generally divided into three categories which are error rate-based, data distribution-based, and multiple hypothesis test-based or statistical test-based [18]. The error rate-based drift detection methods continuously monitor the performance of the base classifier. The base classifiers are used to categorize incoming instances in concept drift detection systems. It generates a class prediction for each instance, which is then compared to the actual class label. The drift detection technique states that whether or not a drift has occurred based on the classification result. In the end, the instance is used to train the base classifier. Every time a new instance arrives, this procedure is repeated. The Drift Detection Method (DDM) [28] is a well-known example of an error rate-based approach. It increases or decreases the value or error rate based on the classification performance of the base classifier. In the case of correct classification of the incoming instance by the base classifier, DDM decreases the value of the error rate and increases otherwise. When the level of error rate reaches a certain threshold, DDM either considers it as a drift or generates a warning. The DDM produced good results for sudden and gradual drift but not for the slow, gradual drifts. The Early Drift Detection Method (EDDM) [29], an improved version of DDM, was proposed to increase the performance of slow progressive drifts. Instead of only the error rate, the EDDM tracks the average distances of two errors as well as the two running averages of standard deviations. The EDDM, like the DDM, has two threshold values to designate the warning zone and the drift zone. The reactive Drift Detection Method [30] (RDDM) periodically reduces the number of instances of a very long concept and recalculates the DDM stats to overcome the performance loss issue of DDM. The ADWIN [31] algorithm uses sliding windows to compare the mean of the output sequences that may vary with time. By analyzing the average of a statistic across the two windows, an algorithm will determine the window's size and shape. Whenever the absolute value of the difference between the two averages exceeds a certain threshold, a change is recognized, and it resets the output windows. The data distribution-based drift detection methods mostly use sliding windows to keep the recent data samples and apply statistics to identify the dissimilarity between two distributions in old and new data samples. Most traditional concept drift detection approaches combine learner (classifier) the current distribution and the dissimilarity measurement method (drift detector) to determine modifications and changes between the current and previous distributions. Distribution-based approaches may identify both real and virtual drifts, depending on the kind of dissimilarity metrics used. They may, however, be unable to discriminate the kinds of observed drifts [32]. [PERSON] et al. [33] presented a distribution-based drift detection method called kdqTree. This approach divides the distributions into smaller substructures. The substructures from the prior and most current data windows are compared using the Kullback-Leibler distance matrix to assess the drift. The study [34] proposes a concept drift detection approach that is based on the premise that there is restricted access to class labels or no access at all. When applied to unlabeled data streams, the suggested technique identifies concept drift based on the class label information that has been anticipated by the base classifier.