Data Refinement

Data refinement focuses on enhancing the quality and suitability of datasets for machine learning tasks, addressing issues like data scarcity, noise, and inconsistencies. Current research explores diverse refinement strategies, including generative adversarial networks (GANs) for data augmentation, robust regression techniques for improving data accuracy, and sophisticated data cleaning pipelines for large-scale datasets. These advancements are crucial for improving the performance and reliability of various machine learning models, particularly in applications like anomaly detection, natural language processing, and federated learning, where data quality significantly impacts model accuracy and privacy.