# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Methods
#### 3.2.1 Oversampling  
Oversampling techniques are data augmentation methods used in machine learning to address imbalanced data classification. In such scenarios, the minority class (SMS group) has significantly fewer instances than the majority class (normotypic group), leading to model bias and poor performance in identifying the minority class. The goal of oversampling is to balance the class distribution by increasing the minority class instances in the training dataset, thus improving model performance and reducing bias. This research uses several oversampling techniques: ROS, SMOTE, and variants. This manuscript considers a broad range of techniques for the synthetic generation of samples to handle highly imbalanced data, which are well described in the literature and for which software implementations are available [90; 91]. Moreover, the default parameter values were used for each method. For example, the default values for the differential evolution algorithm are five neighbors, thirty clusters for cleansing, a crossover rate of 0.5 (regulates the degree of information exchange between vectors in the differential evolution process), and a similarity threshold of 0.5 (determines how similar samples must be within a cluster to consider removing redundant ones). The only modified parameter is the proportion, which is set to 0.75. Random oversampling (ROS) focuses on the SMS class, the subset with the fewest instances, by duplicating its data points. This simple technique increases the minority class instances, potentially improving the model's ability to classify it accurately. However, replicating data points can lead to overfitting, resulting in suboptimal performance with new data as overfitting. Furthermore, the expansion of the dataset translates to an increased volume of data for the model to undergo training, consequently prolonging the training duration [92]. Synthetic minority oversampling technique (SMOTE) addresses class imbalance by generating synthetic instances for the minority class (SMS) [93]. By creating new data points similar to existing ones, SMOTE increases the minority class volume, enhancing model efficacy. However, SMOTE can introduce noise and overfitting, especially with overlapping class distributions or noisy datasets. Additional techniques may be needed to mitigate these risks. Therefore, some extra techniques should be included and they are described below. 1. Adaptively density oversampling along the first principal component axis (ADOMS) addresses the challenge of class imbalance in a sophisticated manner. This method generates synthetic examples for the minority class. Notably, these synthetic samples are strategically positioned along the first principal component axis of the local data distribution. This targeted approach ensures that the generated samples effectively augment the representation of the minority class in the training data. Consequently, ADOMS leads to improved performance by the classification model [94]. 2. Assembled SMOTE distribution aims to minimize class overlap by considering the distribution of borderline minority class samples. This method creates new synthetic data points for each subgroup within the minority class. By incorporating nonborderline data points into the search for neighbors of borderline samples, especially in cases of uneven data distribution, the method ensures a pool of at least k-neighbors for comparison [95]. 3. The combined cleaning and resampling (CCR) methodology adopts a holistic approach towards data preprocessing, integrating two distinct techniques: cleaning and resampling. The former facet entails the identification and subsequent elimination of noisy or irrelevant data points, with particular emphasis on those originating from the majority class. Such detailed curation aims to mitigate potential model misguidance stemming from erroneous data. The latter facilitates the oversampling of the minority class [96]. 4.