[论文] Fifty-Two Shades of Pig: What the UCR Archive Really Asks Classifiers ...

论文概要

研究领域: ML 作者: Omar Swelam, Jaris Küken, Shi Bin Hoo, Frank Hutter, Lennart Purucker 发布时间: 2026-11-06 arXiv: 2611.09843

中文摘要

猪在失血——这就是实验的目的：气道压力能否在失血变成危象之前发出预警？在UCR时间序列分类档案库（TSC的事实标准基准）中，同样的录音被用来问一个不同的问题："这条轨迹来自52头猪中的哪一头？"PigAirwayPressure 数据集并非个例。尽管该档案库是标准基准，但其任务是否与 TSC 本应解决的问题相似却鲜有检视。作者发现多个数据集被从原始任务挪用、基于玩具设置构建，或存在缺陷的测试划分。作者还质疑档案库中有序的非时间序列（如光谱、形状轮廓）对真实TSC的代表性，以及多变量问题分解可能带来的弊端。这些发现呼吁对档案库整体得分更谨慎的解读，并对部署相关的评估给予更多关注。

原文摘要

The pigs were bleeding. That was the point of the experiment: could airway pressure reveal hemorrhage before it became critical? In the UCR Time Series Classification Archive, the de facto benchmark for Time Series Classification (TSC), the same recordings ask a different question: "which of 52 pigs produced this trace?". The dataset PigAirwayPressure is not an outlier. Although the archive is the standard benchmark, whether its tasks resemble the problems TSC is meant to solve has rarely been examined. We find that several datasets are repurposed from their original task, built from toy setups, or have flawed test splits. We further question how representative the archive's ordered non-temporal sequences (e.g. spectra, shape outlines) are of real TSC, and the potential downsides from the ...

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