# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Methods
#### 3.2.1 Oversampling
It involves three steps. the first one, LVQ, creates codebooks representing prototypical data points for each feature. The second one, uses these codebooks to guide the generation of synthetic data points towards regions with similar attributes to the minority class. Finally, LVQ-SMOTE then interpolates to create new synthetic data points within a \"safe geometric region\" around the original data point. However, defining the codebooks and their parameters adds complexity compared to standard SMOTE [104]. * 12. Normal distribution oversampling (NDO-Sampling) uses a random walk from existing minority class data points to generate synthetic samples. This method ensures that the synthetic samples closely match the original minority class data's expected value and squared deviation. It mitigates intraclass similarity and addresses data imbalance efficiently without needing to find the k-nearest neighbors. This approach helps avoid overfitting and generates new data points that resemble the original minority class data without complex neighbor searching algorithms [105]. * 13. Noncooperative game theory for filtering oversampled data (NEATER), introduced in [106], exploits game theory principles for data refinement. This method discards data points within the oversampled set that exhibit a heightened likelihood of belonging to the opposing class. The likelihood of class membership is determined through an analysis of each instance's neighborhood within the data. However, this process can introduce noise or redundant data. NEATER aims to filter out these unwanted elements [106]. * 14. Proximity weighted synthetic oversampling technique (ProWSyn) enhances SMOTE by using proximity weighting, assigning higher weights to minority class data points near the decision boundary. This prioritizes generating synthetic data in critical regions, improving the model's ability to recognize the minority class. By focusing on the decision boundary, ProWSyn can better capture the minority class's intricacies and improve classification accuracy. Additionally, it helps avoid generating synthetic points in noisy regions, unlike traditional SMOTE. The effectiveness of ProWSyn depends on the method used to calculate and apply proximity weights, requiring careful calibration for optimal performance [107]. * 15. Synthetic minority oversampling based on data density (SMOBD) employs a range of techniques. It includes traditional (i) support vector machines (SVMs) where SMOBD leverages this information to generate new data points that line the decision boundary, focusing on areas where the minority class is scarce: (ii) SMOTE combined with edited nearest neighbors (SMOTE-ENN), which helps to remove these noisy data points before applying SMOTE; and (iii) SMOTE with varying costs for SVM (SDC) that incorporates class imbalance information by assigning different costs to misclassifying data points during the SVM training process [108]. 16 Supervised SMOTE. The primary objective of supervised oversampling (supervised SMOTE) lies in achieving a more balanced distribution within datasets. This objective is accomplished by synthesizing additional data samples that belong to the minority class. The process of generating these synthetic samples is guided by the existing data points, hence the term \"supervised\" [109].