用神经网络注意力图引导大模型发现时序数据中的隐藏类别。
Saliency Map-Guided Knowledge Discovery for Subclass Identification with LLM-Based Symbolic Approximations
- 通过梯度显著性图将多分类转为二分类，提取关键信号特征。
- 在多个数据集上，新方法在聚类和子类识别上均优于纯信号基线。
- 适合需要解释性、探索隐藏结构的工业传感器数据分析场景。
本文提出一种新颖的神经符号方法，用于基于传感器信号的知识发现，聚焦于时序分类任务中潜在子类的识别。该方法利用训练好的神经网络生成的基于梯度的显著性图引导发现过程。将多分类问题通过标签聚合转化为二分类问题，并为每个子问题训练分类器以获得显著性图。按预测类别分组输入信号，在三种不同配置下进行聚类。最终聚类中心作为输入送入大语言模型（LLM）进行符号近似与模糊知识图匹配，从而发现原始多分类问题的潜在子类。在多个标准时序分类数据集上的实验结果表明，该显著性图驱动的方法在知识发现方面有效，相较于仅依赖信号的基线方法，在聚类和子类识别上均有提升。
原文摘要 · Abstract (English)
This paper proposes a novel neuro-symbolic approach for sensor signal-based knowledge discovery, focusing on identifying latent subclasses in time series classification tasks. The approach leverages gradient-based saliency maps derived from trained neural networks to guide the discovery process. Multiclass time series classification problems are transformed into binary classification problems through label subsumption, and classifiers are trained for each of these to yield saliency maps. The input signals, grouped by predicted class, are clustered under three distinct configurations. The centroids of the final set of clusters are provided as input to an LLM for symbolic approximation and fuzzy knowledge graph matching to discover the underlying subclasses of the original multiclass problem. Experimental results on well-established time series classification datasets demonstrate the effectiveness of our saliency map-driven method for knowledge discovery, outperforming signal-only baselines in both clustering and subclass identification.
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