SSD

Synthetic Minority Over-sampling Technique (SMOTE) helps fix class imbalance in machine learning by generating synthetic samples for the minority class. Recent research has explored various modifications and extensions of SMOTE to further enhance its effectiveness. SMOTE-ENC, for example, encodes nominal features as numeric values and can be applied to both mixed datasets and nominal-only datasets. Deep SMOTE adapts the SMOTE idea in deep learning architecture, using a deep neural network regression model to train the inputs and outputs of traditional SMOTE. LoRAS, another oversampling approach, employs Localized Random Affine Shadowsampling to oversample from an approximated data manifold of the minority class, resulting in better ML models in terms of F1-Score and Balanced accuracy. Generative Adversarial Network (GAN)-based approaches, such as GBO and SSG, have also been proposed to overcome the limitations of existing oversampling methods. These techniques leverage GAN's ability to create almost real samples, improving the performance of machine learning models on imbalanced datasets. Other methods, like GMOTE, use Gaussian Mixture Models to generate instances and adapt tail probability of outliers, demonstrating robust performance when combined with classification algorithms. Practical applications of SMOTE and its variants can be found in various domains, such as healthcare, finance, and cybersecurity. For instance, SMOTE has been used to generate instances of the minority class in an imbalanced Coronary Artery Disease dataset, improving the performance of classifiers like Artificial Neural Networks, Decision Trees, and Support Vector Machines. In another example, SMOTE has been employed in privacy-preserving integrated analysis across multiple institutions, improving recognition performance and essential feature selection. In conclusion, SMOTE and its extensions play a crucial role in addressing class imbalance in machine learning, leading to improved model performance and more accurate predictions. As research continues to explore novel modifications and applications of SMOTE, its impact on the field of machine learning is expected to grow, benefiting a wide range of industries and applications.

Spatial-Temporal Graph Convolutional Networks (ST-GCN) capture complex relationships in graph-structured data, enabling deep learning for varied applications. Graph-structured data is prevalent in many domains, such as social networks, molecular structures, and traffic networks. Spatial-Temporal Graph Convolutional Networks (ST-GCN) are a class of deep learning models designed to handle such data by leveraging graph convolution operations. These operations adapt the architecture of traditional convolutional neural networks (CNNs) to learn rich representations of data supported on arbitrary graphs. Recent research in ST-GCN has led to the development of various models and techniques. For instance, the Distance-Geometric Graph Convolutional Network (DG-GCN) incorporates the geometry of 3D graphs in graph convolutions, resulting in significant improvements over standard graph convolutions. Another example is the Automatic Graph Convolutional Networks (AutoGCN), which captures the full spectrum of graph signals and automatically updates the bandwidth of graph convolutional filters, achieving better performance than low-pass filter-based methods. In the context of traffic forecasting, the Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM) learns the interactions between roadways in the traffic network and forecasts the network-wide traffic state. This model outperforms baseline methods on real-world traffic state datasets and can recognize the most influential road segments in traffic networks. Despite the advancements in ST-GCN, there are still challenges and complexities to address. For example, understanding how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs remains an open question. Moreover, the computational complexity of some graph convolution operations can be a limiting factor in scaling these models to larger datasets. Practical applications of ST-GCN include traffic prediction, molecular property prediction, and social network analysis. For instance, a company could use ST-GCN to predict traffic congestion in a city, enabling better route planning and resource allocation. In the field of drug discovery, ST-GCN can be employed to predict molecular properties, accelerating the development of new drugs. Additionally, social network analysis can benefit from ST-GCN by identifying influential users or detecting communities within the network. In conclusion, Spatial-Temporal Graph Convolutional Networks provide a powerful framework for deep learning on graph-structured data, capturing complex relationships and patterns across various applications. As research in this area continues to advance, ST-GCN models are expected to become even more effective and versatile, enabling new insights and solutions in a wide range of domains.