发表机构
Saxion University of Applied Sciences; University of Twente（萨克逊应用科学大学; 特温特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对时间序列可解释性问题，提出ConceptCF方法，通过时间序列分解构建概念，利用遗传算法生成反事实，经与五种先进方法对比评估，该方法在多指标上表现出色，能有效提供基于概念的反事实解释。
AI 中文摘要
本文提出了ConceptCF，一种基于人类可解释概念进行反事实生成的方法。在医疗保健和预测性维护等高风险领域，人工智能模型能提高效率和安全性，可解释性是确保这些模型依赖因果关系而非虚假相关性的关键。反事实解释能识别改变模型预测所需的最小修改。现有时间序列方法在单个点或子序列上操作，无法确保突变的可解释性。ConceptCF通过修改有意义的概念来提供解释，概念通过时间序列分解构建，反事实通过遗传算法生成。与五种先进方法对比评估表明，ConceptCF在有效性、置信度、接近度、稀疏性和合理性指标上始终表现出色。
英文摘要
This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive maintenance, artificial intelligence models can increase efficiency and safety. Explainability is key to ensure these models rely on causal relationships rather than spurious correlations. Counterfactual explanations identify minimal modifications that would change a model's predictions. Existing methods for time series operate on individual points or subsequences without ensuring interpretability of the mutations. ConceptCF instead modifies meaningful concepts. As a result we can provide explanations in terms of these concepts, for example ``the model's prediction would be `Sit' instead of `Walk' if you increase the scale of the movement''. In this paper, the concepts are constructed through time series decomposition, resulting in concepts such as scale, and frequency bands. Counterfactuals are generated using a genetic algorithm that optimizes the concept mutations. Evaluation against five state-of-the-art approaches demonstrates that ConceptCF consistently achieves top-tier performance across validity, confidence, proximity, sparsity and plausibility metrics.
