## 2 Related Studies
### Techniques for Handling Data Imbalance  
The lack of density in the training dataset, the presence of small disjuncts, and the overlap between classes are some of the drawbacks that may arise when dealing with imbalanced datasets [8]. To prevent oversampling methods from adding information to the dataset, the Synthetic Minority Oversampling Technique (SMOTE) was first introduced by [PERSON] et al. [36]. In this way, the replacement of minority class instances is avoided in favor of the generation of \"synthetic\" examples. The described method randomly selects a nearest neighbor of a minority instance and linearly generates synthetic examples based on the original instance and a nearest neighbor. Several methods which adopt the SMOTE in combination with other techniques for improving the achieved performance are indicated in the literature [37; 38].  
Nowadays, with the recent advancements in the field of generative modeling, traditional methods such as SMOTE have been replaced by Generative Adversarial Networks (GANs). In [39], [PERSON] et al. have evaluated the effects of introducing GAN-based data augmentation during the training stage of Boosting, Bagging, Linear, and Non-linear classifiers for cancer prediction. The study underlined the struggle encountered by typical models in dealing with imbalance data and highlighted the benefits coming from GAN-based resampling.  
A novel data augmentation scheme named the Conditional Generative Adversarial Network Minority-class-augmented Oversampling Scheme (CTGAN-MOS) is introduced in [40] for solving class imbalance problems. The proposed method does not only take into account wrong data distribution, but it also identifies data quality problems. Noisy samples are removed from the starting dataset by means of a coin-throwing algorithm.  
[PERSON] et al. [41] claim that most of the existing oversampling methods augment the data by relying only on the intra-class information of minority classes, while ignoring the inter-class relationships with the majority ones. To overcome this, a novel oversampling model is conceived by the authors, named Majority-Guided VAE (MGVAE). MGVAE generates new samples in minority classes under the guidance of a majority-based prior. Therefore overfitting is avoided because new samples inherit the diversity and richness of the majority ones. To account for limited data too, MGVAE is first pre-trained on majority samples and then fine-tuned on minority classes.  
The joint problem of data balancing and fairness is analyzed in [42]. Fair OverSampling (FOS) leverages SMOTE to reduce class imbalance and feature blurring to increase group fairness. FOS is designed both to balance the cardinality of classes and to de-bias protected features via feature blurring.  
The study carried out in [43] focuses on the effectiveness of GAN-based oversampling methods in tackling the problem of the poor classification performance of minority classes. For this reason, a benchmark is performed among multiple GAN architectures. In more detail, the results achieved by CTGAN, Copula-GAN, WGAN-GP, and DraGAN across classical (KNN, Decision Tree, and Logistic Regression) and ensemble machine learning (XGBoost, Random Forest, and LightGBM) models are compared to identify their strengths and weakness in enhancing classification performance.