Real World Datasets

Real-world datasets are crucial for training and evaluating machine learning models, but their inherent complexities—including noise, bias, missing data, and class imbalance—pose significant challenges. Current research focuses on developing robust methods for handling these issues, encompassing techniques like synthetic data generation using large language models, improved data preprocessing (e.g., rank transformation), and novel algorithms for clustering and anomaly detection in diverse data structures (e.g., time series, event sequences, and knowledge graphs). These advancements are vital for improving model accuracy, reliability, and fairness across various applications, from climate prediction and autonomous driving to fraud detection and fake news mitigation.