面向海事可信决策的可解释神经模糊预测
Explainable Neuro-Fuzzy Prediction for Trustworthy Decision-Making in Maritime
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- University of Thessaly（色萨利大学）
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中文总结 AI 辅助
本文提出一个结合神经模糊预测与两阶段可解释组件的通用框架，在多个基准数据集上实现高达99%的AUC-ROC，并支持特征级和规则级解释，提升海事决策的可信度。
中文摘要 AI 辅助
预测海事系统何时需要维护可能至关重要，可以避免危险和代价高昂的后果。为解决这一问题，本文提出了一种可解释的决策框架，该框架将神经模糊预测模型与一个两阶段的可解释组件相结合。该组件的第一阶段利用基于梯度的显著性图生成特征归因解释，第二阶段使用模糊决策树提取局部规则。所提出的框架具有通用性，可以集成到任何基于深度学习的方法中，使其变得可解释。据我们所知，这是首个能够对黑盒模型提供特征级和局部基于规则的解释的基于模糊逻辑的框架。该方法旨在通过用户可理解的机器推理来促进决策中的可信度。使用基于深度残差的神经主干，所提出的框架在多种通用公共基准数据集上进行了性能评估，并在海军推进系统数据集的早期故障检测背景下展示了其在海事领域的实用性。结果表明，该框架能够提供优于相关最先进方法的预测，平均AUC-ROC（接收者操作特征曲线下面积）值最高可达99%，同时具有可解释性的优势。
英文摘要
Predicting when maritime systems require maintenance can be critical, avoiding hazards and costly consequences. To address this problem, this paper proposes an explainable decision-making framework that integrates a neuro-fuzzy prediction model with a two-stage explainable component. The first stage of this component produces feature-attribution explanations, using gradient-based saliency maps, and the second stage extracts local rules using a fuzzy decision tree. The proposed framework is generic and can be integrated into any deep learning-based approach, rendering it explainable. To the best of our knowledge, this is the first fuzzy logic-based framework enabling both feature-level and local rule-based explanations of black box models. This approach aims to foster trustworthiness in decision making through user-understandable machine inferences. The performance of the proposed framework using a deep residual-based neural backbone is evaluated on various general-purpose public benchmark datasets, and its utility in maritime is demonstrated in the context of early fault detection in a naval propulsion system dataset. The results indicate that it can provide predictions outperforming relevant state-of-the-art approaches, with an average AUC-ROC (Area Under the Receiver Operating Characteristic Curve) value, reaching up to 99%, while offering the advantage of explainability.
