Adapting the unsupervised uTRAND framework to incorporate domain-specific knowledge or user feedback for refining anomaly detection and classification rules can be achieved through the following strategies:
Semi-Supervised Learning: Introducing a semi-supervised learning approach where domain experts provide annotations or feedback on detected anomalies can help refine the anomaly detection rules. By incorporating labeled data selectively, the framework can learn from both unsupervised observations and domain-specific knowledge.
Interactive Learning: Implementing an interactive learning interface that allows users to provide feedback on detected anomalies can enable real-time refinement of the anomaly classification rules. User feedback can be used to update the rule set, adjust thresholds, or incorporate domain-specific constraints.
Knowledge Graph Integration: Integrating domain-specific knowledge graphs or ontologies into the anomaly detection process can enhance the interpretability and accuracy of the rules. By leveraging structured domain knowledge, the framework can make informed decisions based on contextual information provided by the knowledge graph.
Continuous Learning: Implementing a continuous learning mechanism that adapts the anomaly detection rules over time based on evolving patterns and feedback can improve the framework's adaptability to changing traffic dynamics. By incorporating feedback loops and reinforcement learning techniques, the framework can iteratively improve its performance.
By integrating domain-specific knowledge, user feedback mechanisms, knowledge graphs, and continuous learning strategies, the uTRAND framework can evolve into a more adaptive and refined system for anomaly detection in traffic trajectories.