Multi Class Classification

Multi-class classification, the task of assigning data points to one of several categories, is a core problem in machine learning with applications across diverse fields. Current research focuses on improving model accuracy and efficiency, particularly for high-dimensional data and imbalanced datasets, exploring architectures like deep neural networks, gradient boosting machines, and transformers, as well as techniques such as hierarchical classification and self-training. Significant efforts are also dedicated to enhancing model interpretability and robustness, addressing challenges like adversarial attacks and the need for reliable uncertainty quantification. These advancements are crucial for improving the reliability and trustworthiness of machine learning systems in various applications, from medical diagnosis to fraud detection.