SG-CF: Shapelet-Guided Counterfactual Explanation for Time Series Classification
SG-CF: Shapelet-Guided Counterfactual Explanation for Time Series Classification
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DOI：
10.1109/bigdata55660.2022.10020866
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Peiyu Li;O. Bahri;S. F. Boubrahimi;S. M. Hamdi
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文献类型：
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作者：
Peiyu Li;O. Bahri;S. F. Boubrahimi;S. M. Hamdi
可解释人工智能（XAI）方法最近获得了很大的发展势头，因为它们能够揭示不透明机器学习模型的决策功能。XAI主要有两种范式：特征归因和反事实解释方法。虽然第一类方法解释了模型为什么做出决策，但反事实方法旨在回答如果输入略有不同并导致另一个分类决策的问题。大多数的研究工作都集中在回答为什么时间序列数据模态的问题。在本文中，我们的目的是回答如果问题，找到一个良好的平衡之间的一组理想的反事实解释属性。我们提出了Shapelet引导的反事实解释（SG-CF），这是一种新的基于优化的模型，可以生成时间序列分类模型的可解释的，直观的事后反事实解释，这些模型可以平衡有效性，接近性，稀疏性和连续性。我们在九个真实世界的时间序列数据集上的实验结果表明，与其他竞争基线相比，我们提出的方法可以生成平衡所有理想的反事实属性的反事实解释。
EXplainable Artificial Intelligence ( XAI) methods have gained much momentum lately given their ability to shed the light on the decision function of opaque machine learning models. There are two dominating XAI paradigms: feature attribution and counterfactual explanation methods. While the first family of methods explains why the model made a decision, counterfactual methods aim at answering what-if the input is slightly different and results in another classification decision. Most of the research efforts have focused on answering the why question for time series data modality. In this paper, we aim at answering the what-if question by finding a good balance between a set of desirable counterfactual explanation properties. We propose Shapelet-guided Counterfactual Explanation (SG-CF), a novel optimization-based model that generates interpretable, intuitive post-hoc counterfactual explanations of time series classification models that balance validity, proximity, sparsity, and contiguity. Our experimental results on nine real-world time-series datasets show that our proposed method can generate counterfactual explanations that balance all the desirable counterfactual properties in comparison with other competing baselines.