改进时间序列分类器WEASEL 2.0的超参设置，提升效率且保持精度
Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule
- 提出自适应集成规模规则，根据序列长度和类别数动态调整
- 在114个数据集上保持平均92.8%中位准确率，内存减少37MB（中位）
- 适用于长序列时间序列分类，尤其适合资源受限场景
WEASEL 2.0是一种基于字典的时间序列分类器，结合扩张滑动窗、随机超参集成与固定大小的密集特征表示。其最大集成规模和最大窗口尺寸的设定采用简单阈值规则，但原论文未提供实证依据。本文在114个UCR数据集上复现WEASEL 2.0，平均准确率达0.865，中位为0.928，与发表值接近（威尔科克斯符号秩检验，p=0.655）。测试了四个设计选择的敏感性：下游分类器、无特征加权、最大窗口尺寸规则、最大集成规模规则。前三者对扰动稳健，第四项在长序列数据集上过度配置，因此提出一种自适应规则，根据序列长度和类别数确定最大集成规模。在固定长度数据集上评估，该规则使峰值拟合内存中位减少37MB（均值395MB），拟合时间中位减少0.4秒（均值4秒），中位准确率变化为0%（均值-0.11%）。内存与时间节省主要集中在长序列数据集，原规则在此类数据上分配最大集成规模。
原文摘要 · Abstract (English)
WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size dense feature representation. Two of its hyperparameter choices, the maximum ensemble size and the maximum window size, are specified by simple thresholding rules whose chosen thresholds are not empirically justified in the original paper. In this work we reproduce WEASEL 2.0 on 114 UCR datasets, achieving a mean accuracy of 0.865 and median of 0.928, closely matching the published values (Wilcoxon signed-rank, p = 0.655). We then test the sensitivity of four design choices: the downstream classifier, the absence of feature weighting, the maximum window-size rule, and the maximum ensemble-size rule. The first three are robust to perturbation. The fourth is over-provisioned for long-series datasets, motivating an adaptive rule that sets the maximum ensemble size from series length and number of classes. Evaluated on fixed-length datasets, the adaptive rule reduces peak fit memory by a median of 37 MB (mean 395 MB) and fit time by a median of 0.4 s (mean 4 s), with a median accuracy change of 0% (mean -0.11%). Memory and time savings concentrate on long-series datasets where the original rule allocates the largest ensemble size.
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