# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 4 Performance Evaluation of Concept Drift
### Datasets  
Real datasets can effectively demonstrate the generality and applicability of the algorithm in the real world, for which the commonly used datasets are KDD CUP 1999, Electricity, Weather, Spam, and CoverType. The KDD CUP 1999 [94] is the dataset used in the KDD (knowledge discovery and data mining) competition. It is mainly used for network intrusion detection to distinguish between normal network connections and malicious network connections, including various attack data simulated in the military network environment. Electricity [95] is derived from the electricity market of New South Wales, Australia (1996-1998). It is mainly used to predict changes in electricity prices in the past 24 h, including the weather, user demand, supply conditions, and seasons. Weather [96] contains daily weather measurement data for a certain area from 2006 to 2016, including temperature, humidity, wind direction, wind speed, visibility, atmospheric pressure, etc., for predicting rainfall. Spam [97] is mainly used to identify spam. CoverType [94] is derived from the forest cover of a certain area in the U.S. Forest Service system.  
Synthetic datasets can evaluate the performance of the algorithms under different concept drift situations and contain various types of concept drift. For detecting abrupt concept drift, R.MNIST [98], P.MNIST [60], and SEA [99] can be used. SEA contains three features and two classes in each sample. R.MNIST and P.MNIST are generated from the MNIST dataset containing 784 features and 10 classes. It is worth noting that P.MNIST also detects recurring drift. For detecting gradual concept drift, Circles [100], Hyperplane [101], and LED [94] can be used. Circles contains two features and two classes in each sample. Then, Hyperplane also detects incremental drift, containing 10 features and 2 classes in each sample. LED also detects abrupt drift, containing 24 features and 10 classes in each sample.  
In addition to the above commonly used datasets, there are some special datasets for deep learning frameworks, such as the Vxheaven dataset [102] commonly used in previous malware analysis studies, consisting of Windows binaries belonging to malware and benign portable executables, containing different types of malware families, such as trojans, ransomware, and viruses. HAR-UCI [103] was made from recordings of 30 subjects performing activities of daily living. The STL-10 dataset [104] is an image recognition dataset for the development of unsupervised feature learning, deep learning, and self-learning algorithms. The Cat-Dog dataset [105] contains two classes, cats and dogs, with 12,500 images. The CIFAR100 dataset [106] is utilized to simulate the distribution drifting situation. It has 60,000 32 \(\times\) 32 \(\times\) 3 RGB images. Finally, some researchers have used their own collected data, as well as datasets from the application domain. For example, the I-LSTM and ECNN algorithms are collected data, and CausalLSTM uses the HDFS dataset [107] and the Cybersecurity's Intrusion Detection Evaluation dataset [108].