Adaptive Machine Learning for Scientific Anomaly Detection

Design a self-evolving machine learning system for detecting rare events and anomalies across scientific datasets with minimal human intervention. Implement advanced active learning techniques, support for transfer learning across different experimental domains, and robust uncertainty estimation. The system must handle imbalanced datasets and provide interpretable explanations for detected anomalies.