基于动态时间规整的粒球计算实现鲁棒高效的含噪标签时间序列分类
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
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中文总结 AI 辅助
针对含噪标签时间序列分类问题，提出DTW-GBC方法，通过构建粒球减少比较次数，在缓解标签噪声影响的同时提升推理效率，取得鲁棒性与效率的平衡。
中文摘要 AI 辅助
基于动态时间规整（DTW）的最近邻（NN）分类器对时间序列分类效果良好，但易受训练样本标签错误影响，且推理过程需进行大量DTW计算。本文提出基于DTW的粒球计算（DTW-GBC），该方法将时间上相似的训练样本组织成粒球，在粒级别执行分类，还开发了两种DTW-GBC的粒球构建策略。在四个含对称标签噪声的基准数据集上开展实验，结果显示，两种DTW-GBC变体通常能缓解标签噪声导致的性能下降，同时推理时所需的比较次数远少于基于DTW的1-NN。这些发现表明，DTW-GBC在分类鲁棒性与推理效率间实现了良好平衡。
英文摘要
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.
发表机构
- Nagoya Institute of Technology（名古屋工业大学）
机构由 AI 辅助整理，请以论文原文为准。
