M-CELS：基于学习显著性图引导的多变量时间序列数据反事实解释
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
AI总结：
本文提出M-CELS，一种基于学习显著性图引导的反事实解释模型，用于提升多变量时间序列分类的可解释性，并在七个真实数据集上优于现有基线。
摘要：
在过去十年中，多变量时间序列分类受到了广泛关注。用于多变量时间序列分类的机器学习（ML）模型取得了显著进展，并在广泛的应用和任务中取得了令人瞩目的成功。许多最先进的ML模型面临的挑战是缺乏透明度和可解释性。在这项工作中，我们引入了M-CELS，一种反事实解释模型，旨在增强多维时间序列分类任务的可解释性。我们的实验验证涉及将M-CELS与领先的最先进基线进行比较，利用来自UEA存储库的七个真实世界时间序列数据集。结果表明，M-CELS在有效性、邻近性和稀疏性方面表现出优越的性能，增强了其在为应用于多变量时间序列数据的机器学习模型提供透明见解方面的有效性。
英文摘要：
Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretability. In this work, we introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Our experimental validation involves comparing M-CELS with leading state-of-the-art baselines, utilizing seven real-world time-series datasets from the UEA repository. The results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity, reinforcing its effectiveness in providing transparent insights into the decisions of machine learning models applied to multivariate time series data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
