Multi Class Classification Task

Multi-class classification, the task of assigning data points to one of several categories, is a core problem in machine learning with applications spanning diverse fields. Current research emphasizes improving accuracy and robustness, particularly focusing on hierarchical classification methods, advanced algorithms like boosting and those leveraging graph-based feature selection, and the application of deep learning architectures such as convolutional neural networks and transformer models (including fine-tuned large language models). These advancements aim to address challenges like imbalanced datasets, high dimensionality, and the need for interpretability and fairness, ultimately leading to more accurate and reliable classification systems across various domains.