An Improved Case-Based Reasoning Method Based on Dynamic Entropy Weighting: A Case Study of Scenario Prediction for Major Maritime Emergencies

The retrieval mechanism of traditional CBR methods has limitations when applied to complex multi-layer objects, mainly due to their nonlinearity and emergent properties. This paper proposes an improved case-based reasoning method with cascaded retrieval (CRCBR) to address the aforementioned issues, but the non-fixed model structure makes it difficult to determine appropriate weight calculation methods for CRCBR. To tackle this problem, we propose a dynamic entropy weight method for heterogeneous multi-attribute data, which designs separate entropy calculation methods for different data types and computes weights through a flexible secondary allocation mechanism. Finally, using seven evaluation metrics and three assessment perspectives as the evaluation framework, we validate the effectiveness and superiority of our method through comparative experiments with traditional CBR and CRCBR without dynamic entropy weighting (termed the baseline method) on real-world maritime emergency data. The experimental results show that our method surpasses traditional CBR across all three evaluation perspectives, outperforms the baseline in two perspectives, and is no worse than the baseline in the remaining perspective.