Balanced Sampling

Balanced sampling techniques aim to mitigate the negative effects of imbalanced datasets in machine learning, improving model performance and generalization by ensuring fair representation of all data classes. Current research focuses on developing novel sampling strategies, such as cluster-based and balance-aware methods, often integrated with advanced model architectures like graph neural networks and diffusion models, to address class imbalance in diverse applications. These advancements are crucial for enhancing the reliability and fairness of machine learning models across various domains, including natural language processing, computer vision, and knowledge graph completion, leading to more robust and equitable outcomes.