## 2 Related Work
The Geometric Moving Average [35] (GMA) gives the most recent observation the greatest weight, and all previous observations' weights decrease in geometric progression. A novelty detection algorithm in multi-class streams MINAS was proposed in [27], which separates the learning process into two stages: offline and online. The offline phase trains a decision model based on the problem's known concept. It is only used once. Following that, the online phase collects new samples and categorizes them as belonging to one of the existing classes or as unknown. In addition, the algorithm seeks a coherent collection of unknown samples to find new classes or expansions of established classes. These additional classes and extensions are included in the decision model. Furthermore, the approach enables the model to ignore out-of-date data and automatically adjust to concept drift. Page Hinckley (PH) [36] is a sequential analysis test that monitors the difference between current accuracy and means accuracy of the base classifier for a current moment. If the difference between two accuracies reaches at a threshold, it is considered as a drift. The Page-Hinckley test was used in [37] to detect the change in normal and abnormal signals. CUSUM [38] is a variant of PH which is a sequential analysis that uses the principles of the Sequential Probability Ratio Test (SPRT). It monitors the mean of the predicted result values and generates the drift alarm if the mean values reach to a certain threshold. The hypothetical test-based drift detection can be applied to input data as well as the output of the ML model. The HDDM_A and HDDM_W were proposed in [39], which are similar to the DDM but use Hoeffiding's inequalities. HDDM_A calculates the moving average, and HDDM_W calculates the weighted moving average from the incoming input stream. They first identify the relevant cut point in the incoming stream based on that they either apply A-test or W-test to find the current state of the stream. The possible options for stream state are stable, warning, or drift. A Statistical Test of Equal Proportions [40] (STEPD) computes the accuracy of the base classifier in the W most recent instances and compares it to its overall accuracy from the beginning of the learning process. Recently, a window-based drift detection approach was proposed [41]. It has ability to store the most recent samples on a sliding window and removes the old ones. The samples of the sliding window are distributed in two sub-windows. One sub-window keeps the most recent samples from the main sliding window and considers it a new concept, whereas the other sub-window keeps the older samples and considers them as an old concept. They further apply the Kolmogorov-Smirnov [42] test to compare the absolute distance of the two sub-windows over a cumulative distribution. A window is a buffer memory to store limited data. This window can be used to store input data or output of the ML model to monitor the behavior of the data or output. Another sliding window-based drift detection method called One-Class Drift Detector (OCDD) was proposed in [43]. Atthe start, a one-class classifier is trained using the data in the sliding window. The one-class classifier is used to estimate the distribution of the new concept, identifying whether fresh samples are from the existing idea or are outliers. Outlier samples are recognized as data from the new concept. It indicates a drift based on the proportion of outliers discovered in the sliding window. This technique is repeated indefinitely until there is no fresh data. OCDD's fundamental flaw is that it identifies drifts redundantly (false positives) when there are virtual drifts in which \(P(X)\) changes, but \(P(y\,|\,X)\) does not. The classifier is excessively adjusted, and the model loses important knowledge. Furthermore, it is incapable of detecting concept drifts (false negatives) in which changes occur solely on \(P(y\,|\,X)\).