迈向时间序列反事实解释的合理性
Towards plausibility in time series counterfactual explanations
AI总结：
本文提出一种基于梯度优化和软动态时间规整的时间序列反事实解释方法，通过增强合理性与分布对齐性提升时序解释的可信度。
摘要：
我们提出了一种生成合理反事实解释的新方法，用于时间序列分类问题。该方法在输入空间中直接进行基于梯度的优化。为了确保合理性，我们整合了软动态时间规整（soft-DTW）对齐与目标类的k近邻，这有效地促使生成的反事实采用现实的时序结构。整体优化目标是一个多方面的损失函数，平衡了关键的反事实属性。它结合了有效性、稀疏性和接近性的损失，以及新的基于soft-DTW的合理性组件。我们对我们的方法进行了评估，与几种强大的参考方法进行比较，测量生成的反事实在多个维度上的关键属性。结果表明，我们的方法在有效性方面实现了竞争性的性能，同时在与目标类的分布对齐方面显著优于现有方法，表明更优越的时序现实性。此外，定性分析突显了现有方法在保持现实时序结构方面的关键局限性。这项工作表明，所提出的方法能够一致地为时间序列分类器生成反事实解释，这些解释不仅有效，而且高度合理，并与时间模式一致。
英文摘要：
We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in the input space. To enforce plausibility, we integrate soft-DTW (dynamic time warping) alignment with $k$-nearest neighbors from the target class, which effectively encourages the generated counterfactuals to adopt a realistic temporal structure. The overall optimization objective is a multi-faceted loss function that balances key counterfactual properties. It incorporates losses for validity, sparsity, and proximity, alongside the novel soft-DTW-based plausibility component. We conduct an evaluation of our method against several strong reference approaches, measuring the key properties of the generated counterfactuals across multiple dimensions. The results demonstrate that our method achieves competitive performance in validity while significantly outperforming existing approaches in distributional alignment with the target class, indicating superior temporal realism. Furthermore, a qualitative analysis highlights the critical limitations of existing methods in preserving realistic temporal structure. This work shows that the proposed method consistently generates counterfactual explanations for time series classifiers that are not only valid but also highly plausible and consistent with temporal patterns.
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