# A New Image Oversampling Method Based on Influence Functions and Weights
## 2 Related Work
### Data Level  
The data level method belongs to the pre-processing stage of data. This method used at this level is independent of the classifier used. The related method is to modify or resample the imbalanced dataset to generate a new balanced dataset and use it for classifier training. This method focuses on the balance of datasets, mainly divided into three methods [8]. (1) Oversampling method: Due to the small sample sizes of the few classes in the unbalanced dataset, this method is based on minority class samples and copies them or identifies sample characteristics and synthesizes new samples to enlarge the sample size of the minority category. (2) Undersampling method: Due to the significant difference in the number of samples of each category in the imbalanced dataset, too many samples of the majority class will affect the samples of the minority class. The undersampling algorithm deletes the number of majority class samples by removing noisy and less informative samples from the majority class, making the dataset a balanced distribution. (3) Hybrid sampling method: This method combines the oversampling method with the undersampling method, deletes some samples in the majority class, and copies or synthesizes some minority class samples to balance the dataset to a certain extent. The principle of the undersampling algorithm is to delete samples in the majority class to achieve the balance of the dataset. However, deleting samples may delete important ones, leading to blurred data boundaries. Furthermore, if the model training is insufficient with the reduction data, the method is unsuitable for imbalanced data. The oversampling algorithm can retain the characteristics of the original data and alleviate the data imbalance problem, such as the SMOTE [7], Borderline, ADASYN [9], etc. SMOTE is an oversampling algorithm studied and improved by many scholars and is widely used in data imbalance problems. However, it still has many problems, such as oversampling a small number of samples with small information content [10] and producing noisy samples [7]; additionally, the model is prone to overfitting [11]. In response to the many problems existing in SMOTE, many scholars have made improvements in recent years. [PERSON] et al. [12] proposed the DeepSMOTE scheme, which takes advantage of the characteristics of the SMOTE algorithm and uses a deep learning model to oversample small class samples. This scheme causes class overlap problems. To solve the class overlap and centrality problems, [PERSON] et al. [13] proposed the FW-SMOTE scheme. The scheme uses the weighted Minkowski distance to define the nearest neighbor samples of each minority class and prioritizes samples with more feature information for sample synthesis. This scheme easily overlooks some critical samples, and the quality of the synthesized samples could be better. To improve the quality of generated samples, [PERSON] et al. [14] proposed the SMOTE-CD scheme, which uses combined data operations to calculate linear combination points of minority class sample points and generates samples based on the combination points, making the generated samples more realistic. With the development of deep learning, related scholars began to study the GAN [15] network, which improves the network structure for generating minority class samples to solve the data imbalance problem. Compared with SMOTE methods, GAN networks achieve higher AUC, accuracy and precision. [PERSON] et al. [16] utilized generative conditional GAN (CGAN) to model the data distribution and generate small class samples. To address the problem of the model's weak generalization ability, [PERSON] et al. [17] proposed a balanced GAN by adding random variables to increase the diversity of the synthetic samples while improving the quality of the generated images. Despite the remarkable achievements of SMOTE and GAN, these methods have many drawbacks. For example, the SMOTE scheme introduces noise, which leads to overgeneralization.