# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Methods
#### 3.2.1 Oversampling
Cluster ensemble synthetic minority oversampling technique (CE-SMOTE) leverages cluster ensemble technology to address class imbalance within datasets. The core principle of this approach involves the introduction of a clustering consistency index. This index is employed to identify data points belonging to the minority class that reside on the periphery of clusters. Once these \"cluster boundary positive samples\" are identified, CE-SMOTE oversamples them to augment the original dataset and achieve a more balanced class [97]. 5. Clustered synthetic minority oversampling technique (Cluster-SMOTE) combines clustering with SMOTE to improve synthetic data generation for the minority class. First, a clustering algorithm like k-means partitions the minority class data into clusters, identifying homogeneous regions. Then, SMOTE is applied within each cluster to generate synthetic data points, enhancing minority class representation. This approach creates more representative synthetic data, potentially improving model performance. However, the effectiveness of Cluster-SMOTE depends on selecting and optimizing the clustering algorithm and its parameters. Additionally, this technique involves an extra clustering step compared to standard SMOTE [98]. 6. Density-based synthetic minority oversampling technique (DBSMOTE) builds on SMOTE by using density-based clustering (DBSCAN) to generate synthetic data points for the minority class. It identifies clusters of minority class data points and creates synthetic instances within these clusters, improving model performance by focusing on dense regions. However, DBSMOTE's effectiveness depends on selecting propriate DBSCAN parameters and may not be advantageous for uniformly distributed minority classes, where standard SMOTE might be more suitable [99]. 7. Differential evolution (DE) is a prominent metaheuristic optimization algorithm that operates on a population of candidate solutions. Its ease of implementation, rapid convergence properties, and robust performance have led to its extensive adoption across various scientific and engineering disciplines. Notably, DE demonstrates particular efficacy in tackling global optimization problems within the numerical domain [100]. 8. Geometric SMOTE (G-SMOTE) uses a geometric approach instead of linear interpolation. It creates a secure geometric domain (like a hyper-sphere or hyper-ellipsoid) around each minority class data point to generate synthetic data points within this domain. This method aims to produce synthetic points that are less prone to noise or outliers and more varied compared to traditional SMOTE. However, accurately defining the geometric region and its parameters adds complexity [101]. Gaussian SMOTE is a variant of the traditional SMOTE algorithm that uses Gaussian distribution principles to generate synthetic data points for the minority class. It extends the basic concept by incorporating Gaussian distribution attributes centered on the minority class data point, which governs the spatial displacement of the new synthetic data points. This method mitigates overfitting by introducing controlled randomness and emphasizes generating synthetic points close to the selected minority class point. However, it requires precise definition of Gaussian distribution parameters, adding complexity [102]. * 10. [PERSON]'s method introduces a location-aware approach for generating synthetic data with a noise-filtering mechanism. It assesses each potential synthetic data point and excludes those identified as noise, eliminating the need to predefine the number of nearest neighbors. This method also uses a rejection level metric to mitigate overfitting and noisy data generation. Synthetic instances with a rejection level of three or more are included, while those below this threshold are rejected to prevent noise [103]. * 11. Learning vector quantization-based synthetic minority oversampling technique (LVQ SMOTE) combines learning vector quantization (LVQ) with SMOTE to improve the generation of synthetic data points for the minority class.