通过分解信号生成更可信的时间序列反事实解释
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
- 将信号分解为固有模态函数，替换其中成分以生成反事实
- 用三组近似异常样本替换后，分类器准确翻转且保持结构合理
- 适合需要可解释性的工业故障检测场景
振荡信号（如振动）在特定频段携带类别判别信息；在原始特征空间扰动此类信号易破坏时序结构，导致物理上不合理的反事实结果。本文提出IMFACT（基于固有模态函数的反事实），一种模型无关的时间序列分类器反事实解释框架，其在经验模态分解（EMD）的分解空间中操作。输入信号被分解为固有模态函数（IMFs），选定的IMFs逐步替换为最近似异常邻居（NUN）的IMFs，直至分类器输出翻转至目标类别。我们在两个UCR基准数据集（FaultDetectionA、FruitFlies）上评估六种IMF选择策略及多NUN循环扩展。基于方差的策略结合三个NUN在可靠性与合理性指标上优于两项主流基线方法，而跨三个NUN的循环机制在两个数据集上均实现最佳接近度。
原文摘要 · Abstract (English)
Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily destroys their temporal structure and produces physically implausible results. In this work, we introduce IMFACT (IMF-based counterfACTuals), a model-agnostic framework for generating plausible counterfactual explanations for time series classifiers that operates in the decomposition space of Empirical Mode Decomposition. An input signal is split into Intrinsic Mode Functions (IMFs), and selected IMFs are progressively substituted with those of a Nearest Unlike Neighbour (NUN) until the classifier flips to the target class. We evaluate six IMF-selection strategies and a multi-NUN cycling extension on two UCR benchmarks (FaultDetectionA, FruitFlies). The variance-based strategy with three NUNs outperforms two prominent baseline techniques on reliability and plausibility metrics, while cycling across three NUNs yields the best proximity across both datasets.
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