Multi Class

Multi-class classification tackles the problem of assigning data points to one of several categories, extending beyond the simpler binary classification. Current research focuses on improving model accuracy and efficiency in handling imbalanced datasets and high-dimensional data, employing techniques like boosting algorithms, deep learning architectures (e.g., CNNs, transformers, and graph neural networks), and novel loss functions designed for multi-class scenarios. These advancements are crucial for various applications, including image analysis, natural language processing, and anomaly detection, where accurate and efficient multi-class categorization is essential for improved decision-making and resource allocation.