# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 2 Overview of Concept Drift
### The Types of Concept Drift  
The changes in concept may manifest in different forms over time. At present, the most popular types of concept drift can be divided into abrupt drift, incremental drift, gradual drift, and recurring drift [14; 16; 17]. Abrupt drift refers to the rapid change of concept \(C_{1}\) into concept \(C_{2}\) in a short period of time, and if an earthquake suddenly occurs in a certain place, its economic model changes instantaneously, as shown in Figure 2a. Incremental drift refers to the slow transformation of concept \(C_{1}\) into concept \(C_{2}\) in a continuous manner, as the economy gradually recovers after an earthquake, as shown in Figure 2b. Gradual drift refers to a short period of time: \(C_{1}\) and \(C_{2}\) repeatedly switch and eventually stabilize at \(C_{2}\), as the equipment ages, occasionally fails, and finally stops working, as shown in Figure 2c. Recurring drift refers to the fact that over time, the previous concept will reappear after a period of time; for example, the sales of down jackets meet concept \(C_{1}\) in the winter, start to enter the off-season after the end of the winter, their sales will meet concept \(C_{2}\), and then the next winter concept \(C_{1}\) will reappear, as shown in Figure 2d. In addition, the speed of recurring drift can be abrupt, gradual, or incremental. It can also be periodic or irregular. In the academic research of concept drift, the types are different according to the classification criteria. However, it is common to divide the types of concepts according to their transformations, and this criteria manifestation is more intuitive. In related studies, different methods adapt to solving different types of concept drift. For example, the drift detection method (DDM) algorithm [21] is more suitable for abrupt drift. In addition to adapting the four common types above, there are some methods for distinguishing real drift and virtual drift, avoiding mixing with virtual concept drift or outliers and noise. For example, the RRBM-DD [22] considers explicitly how to identify the drift of the real concept. Although concept drift adaptation methods cannot solve all types of concept drift at one time, they can still solve multiple concept drifts, which belongs to a one-to-many relationship. In recent years, there have also been many excellent concept drift detection algorithms to detect multiple concept drifts, for example, based on sliding-window algorithms, QCDD [23], CDT_MSW [24], and KSWIN [25]. OCDD mainly has two sliding windows  
Figure 1: The causes of concept drift. Figure 2: The types of concept drift. to store new and old data, and the percentage of outliers detected by the classifier in the sliding window is used to send a drift signal, which is more suitable for detecting abrupt and incremental drift, but hyperparameter settings are required. CDT_MSW also has two windows, the difference being that it can identify the position and length of concept drift, so as to accurately determine the type of concept drift. KSWIN detects concept drift by applying the \"Kolmogorov-Smirnov test\". These algorithms are based on supervised learning. Unsupervised concept drift algorithms include LD3 [26], STUDD [27], and CDCMS [28]. LD3 introduces the concept of label-dependent ordering for concept drift detection in multi-label classification, which is more suitable for mutation and incremental drift. STUDD mainly creates an auxiliary model (students) to mimic the behavior of the main model (teacher), uses the teacher to predict new instances, and monitors the student's imitation loss to detect concept drift. It is more suitable for abrupt, gradual, and incremental drift. CDCMS mainly uses novel clustering and diversity-based memory management strategies in model space strategies to deal with concept drift and has good effects in dealing with abrupt and recurring drift. Finally, it is worth mentioning that most of the concept drift detection algorithms either occupy more memory or have a slow detection speed.