# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 3 Concept Drift Adaptation Methods under Deep Learning
### Concept Drift Adaptation Methods Based on Discriminant Learning
parameters through the Adadelta optimizer, so as to adapt to the abrupt and gradual drift. This algorithm effectively improves the classification performance of highly unbalanced data streams, although its feature selection needs to be optimized. * RNN-based concept drift adaptation methods  
Compared to other neural networks, RNN has certain advantages in processing sequence data because it has at least one feedback connection [37]. To some extent, it can alleviate the problem of concept drift. However, its capacity is also limited, especially when it comes to processing long data. It is mainly used in the fields of electricity loading, weather forecasting, and anomaly detection. Typical algorithms are the online adaptive recurrent neural network (OARNN), ONU-SHO-based RNN (ONU-SHO-RNN), adaptive behavioral-based incremental batch learning malware variant detection model (AIBL-MVD), and multilayer self-evolving recurrent neural network (MUSE-RNN). OARNN [38] mainly uses the RNN model to capture the temporal correlation and track its performance. When the performance deteriorates, the tree-structured Parzen estimator (TPE) will be used to optimize the hyperparameters of the model online. Then, the weights of the RNN model are completely updated and relearned from new data to accommodate concept drift over short periods of time. It is mainly used for energy and electricity load forecasting, although it requires a large amount of data for training and learning. In addition, [PERSON] et al. [39] proposed an online ARIMA-RNN integration based on OARNN, which belongs to hybrid learning. It will be further introduced later. ONU-SHO-RNN [40] determines whether to update the model by calculating its prediction accuracy and the concept drift detection of the RNN model on the data stream. It mainly uses the ONU-SHO algorithm to perform a complete parameter update and narrow the error between the target output and the measurement output. It is capable of fast convergence, adapting to incremental and gradual drift, although there are problems with update delays. In addition, AIBL-MVD [41] also adapts to incremental and gradual drift. It mainly uses the statistical process control (SPC) algorithm to actively detect the occurrence of concept drift and update all model weights through incremental learning. It is mainly used in the field of malware detection. In this process, the catastrophic forgetting problem is solved by mixing the new data with a subset of the old data. Its limitation is that labeled malware samples must be available right before updating the model. All of the above methods are based on parameter update mode. Subsequently, MUSE-RNN [42] mainly uses structural updates to update the models, and it actively detects concept drift through [PERSON]'s bound detection algorithm, which is also a common method in concept drift detection algorithms. The model is updated by using growth and pruning hidden nodes and layers for the real-time classification of data streams, although it does not handle image streams. * LSTM-based concept drift adaptation methods  
Long short-term memory (LSTM) is a variant of RNN that solves problems such as vanishing gradients and is suitable for processing and forecasting important events with relatively long intervals and delays in time series [43]. LSTM-based concept drift adaptation methods are mainly used in the fields of anomaly detection, photovoltaic power generation prediction, and industrial prediction, and their typical algorithms include DL-CIBuild, I-LSTM, multi-objective metaheuristic optimization-based big data analytics with concept drift detection (MOMBD-CDD), adaptive LSTM (AD-LSTM), DCA-DNN, etc. DL-CIBuild [44] is an algorithm based on the LSTM model to construct prediction models for continuous integration (CI) build outcome prediction. It uses the genetic algorithm (GA) to adjust the hyperparameters (including the number of hidden layers and neurons) of the LSTM model. In particular, it does not require a very large dataset size and has good robustness.