XTSC-Bench：时间序列分类解释器的定量基准测试
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification
AI总结：
本文提出XTSC-Bench基准测试工具，通过提供标准化的数据集、模型和指标，系统评估并比较了时间序列分类中11种解释方法，揭示了提升解释器鲁棒性和可靠性的必要性。
摘要：
尽管关于时间序列分类（TSC）可解释机器学习的研究不断增加，但如何评估不同的可解释性方法仍不明确。依赖定性评估和用户研究来评估TSC解释器十分困难，因为人类难以理解时间序列数据中包含的潜在信息。因此，对解释方法进行系统性回顾和定量比较以验证其正确性变得至关重要。尽管针对表格、图像和文本数据已采取了标准化评估的步骤，但对时间序列上的可解释性方法进行基准测试仍具挑战，原因包括：a）传统指标无法直接适用；b）文献中对时间序列传统指标的实现和改编各不相同；c）基线实现存在差异。本文提出XTSC-Bench，这是一个基准测试工具，提供标准化数据集、模型和指标，用于评估TSC上的解释方法。我们分析了应用于TSC的3种基于扰动、6种基于梯度和2种基于示例的解释方法，结果表明有必要提升解释器的鲁棒性和可靠性，尤其是对于多变量数据。
英文摘要：
Despite the growing body of work on explainable machine learning in time series classification (TSC), it remains unclear how to evaluate different explainability methods. Resorting to qualitative assessment and user studies to evaluate explainers for TSC is difficult since humans have difficulties understanding the underlying information contained in time series data. Therefore, a systematic review and quantitative comparison of explanation methods to confirm their correctness becomes crucial. While steps to standardized evaluations were taken for tabular, image, and textual data, benchmarking explainability methods on time series is challenging due to a) traditional metrics not being directly applicable, b) implementation and adaption of traditional metrics for time series in the literature vary, and c) varying baseline implementations. This paper proposes XTSC-Bench, a benchmarking tool providing standardized datasets, models, and metrics for evaluating explanation methods on TSC. We analyze 3 perturbation-, 6 gradient- and 2 example-based explanation methods to TSC showing that improvements in the explainers' robustness and reliability are necessary, especially for multivariate 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。
