Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem that aims to handle situations where each instance may be associated with multiple labels. The core challenges of this task lie in the imbalanced distribution of labels and the vast number of label categories within the dataset. By effectively addressing these challenges, Extreme Multi-Label Classification can significantly improve the accuracy and efficiency of large-scale classification tasks, making it highly valuable, especially in recommendation systems, information retrieval, and natural language processing fields.