## 2 Review
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Research** & **Specification** & **Remarks** \\ \hline \multirow{2}{*}{Oversampling [31; 33; 34]} & \multirow{2}{*}{Data re-sampling technique for imbalanced datasets} & \multirow{2}{*}{Generates random replica samples from the minority class to balance the distribution of the target classes. Increases likelihood of over-fitting.} \\ \hline \multirow{2}{*}{Undersampling [31; 33; 34; 35]} & \multirow{2}{*}{Data re-sampling technique for imbalanced datasets} & \multirow{2}{*}{Reduces the number of samples from the majority class to achieve a more balanced target class distribution. Loss of information through data purging of the original dataset.} \\ \hline \multirow{2}{*}{GPR-based GAN [36]} & \multirow{2}{*}{Data re-sampling and imputation for imbalanced datasets} & \multirow{2}{*}{Combines the use of Gaussian Process Regression and Generative Adversarial Network to input missing data points and generate new samples.} \\ \hline \multirow{2}{*}{Convolutional Neural Network for Automatic Wafer Defect Identification (CNN-WD) [25]} & \multirow{2}{*}{Data re-sampling and feature extraction for imbalanced datasets} & \multirow{2}{*}{Combines CNN feature extraction and oversampling through data augmentation for imbalanced dataset. Data augmentation techniques can be application specific.} \\ \hline \multirow{2}{*}{One-class Fault detection [27]} & \multirow{2}{*}{One-class learning for imbalanced datasets} & \multirow{2}{*}{Multi-network architecture with a fault-detection module based on one-class learning. Challenges scaling up as the number of target classes increase.} \\ \hline \multirow{2}{*}{Cost-sensitive reweighting [23; 40; 41; 42; 43; 44; 24; 45]} & \multirow{2}{*}{Class-balancing weights for imbalanced datasets} & \multirow{2}{*}{Class-balancing weight hyperparameter for loss functions. Reweighting by inverse class frequency has been shown to have limited gains.} \\ \hline \multirow{2}{*}{Focal Loss (FL) [46]} & \multirow{2}{*}{Class-balancing penalty factor for imbalanced datasets} & \multirow{2}{*}{Balances loss for well-classified (easy) vs. misclassified (hard) samples during training, FL loss is more effective to intra-class data imbalance.} \\ \hline \multirow{2}{*}{Label-Distribution Aware Margin (LDAM) loss [47]} & \multirow{2}{*}{Label-dependent regularizer for imbalanced datasets} & \multirow{2}{*}{Label-dependent regularizer that depends on both the weight matrices and the labels for class-balancing.} \\ \hline \multirow{2}{*}{DefectNet for Fault Detection [48]} & \multirow{2}{*}{Class-rebalancing and feature extraction for imbalanced datasets} & \multirow{2}{*}{Combines CNN feature extraction and hybrid loss function for imbalanced dataset. Feature extraction module can be application specific.} \\ \hline \multirow{2}{*}{Transfer Learning-Based Fault Diagnosis [59]} & \multirow{2}{*}{Transfer Learning for imbalanced datasets} & \multirow{2}{*}{Transfer of knowledge from neural networks trained in domains with enough data to others in domains that encounter an imbalanced dataset. Performs well in scenarios where target and source domain a more similar.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of approaches used in tackling imbalanced datasets.