用神经符号方法让雷达手势识别模型既准又可懂。
Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
- 设计新模型RL-Net，用神经优化学可读规则列表。
- 达93.03%准确率，规则复杂度大幅降低。
- 适合需要透明性与性能平衡的边缘传感场景。
基于规则的模型解释性强但处理复杂数据能力差，深度神经网络性能强却缺乏透明性。本文首次将神经符号规则学习网络RL-Net应用于雷达手势识别（HGR），通过神经优化学习可解释规则列表。在与完全透明的规则系统MIRA和可解释黑盒模型XentricAI的对比中，评估了准确率、可解释性和用户适应性（迁移学习）。结果表明，RL-Net在保持93.03% F1分数的同时显著降低规则复杂度，有效平衡了透明性与性能。研究发现规则剪枝与层级偏倚是关键优化挑战，并提出增强稳定性的改进方案。相比两者，RL-Net成为兼具可解释性与实用性的中间路径，验证了神经符号模型在可解释手势识别中的实际可行性，为可解释AI在边缘部署传感系统中的应用提供参考。
原文摘要 · Abstract (English)
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating accuracy, interpretability, and user adaptability via transfer learning. Our results show that RL-Net achieves a favorable trade-off, maintaining strong performance (93.03% F1) while significantly reducing rule complexity. We identify optimization challenges specific to rule pruning and hierarchy bias and propose stability-enhancing modifications. Compared to MIRA and XentricAI, RL-Net emerges as a practical middle ground between transparency and performance. This study highlights the real-world feasibility of neuro-symbolic models for interpretable HGR and offers insights for extending explainable AI to edge-deployable sensing systems.
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