用动态时间规整构建颗粒球，提升时序分类抗噪声能力与推理效率。
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
- 将相似时序样本聚成颗粒球，按粒度分类而非逐点匹配
- 在四个基准数据集上噪声下性能下降减少，推理比较次数显著降低
- 适合需要抗标签噪声和高效推理的工业级时序分类任务
基于动态时间规整（DTW）的最近邻分类器在时序分类中有效，但对错误标签敏感且推理时需大量DTW计算。本文提出基于DTW的颗粒球计算（DTW-GBC），将时间上相似的训练样本组织成颗粒球，在粒度层面进行分类。进一步设计两种颗粒球构建策略。在四个含对称标签噪声的基准数据集上实验表明，两种DTW-GBC变体均能有效缓解噪声导致的性能下降，且推理时所需比较次数远少于基于DTW的1-NN。结果表明，DTW-GBC在分类鲁棒性与推理效率之间取得了良好平衡。
原文摘要 · Abstract (English)
Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference. We propose DTW-based Granular Ball Computing (DTW-GBC), which organizes temporally similar training samples into granular balls and performs classification at the granule level. We further develop two granular-ball construction strategies for DTW-GBC. Experiments on four benchmark datasets with symmetric label noise show that the two DTW-GBC variants generally mitigate the performance degradation caused by label noise while requiring substantially fewer comparisons than DTW-based 1-NN during inference. These findings suggest that DTW-GBC provides a favorable balance between classification robustness and inference efficiency.
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