{"slug": "robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time", "title": "Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing", "summary": "Researchers propose DTW-GBC, a Dynamic Time Warping-based Granular Ball Computing method that organizes temporally similar training samples into granular balls to classify time-series data at the granule level, improving robustness to mislabeled samples and reducing inference computations compared to DTW-based 1-NN. Experiments on four benchmark datasets with symmetric label noise show that DTW-GBC mitigates performance degradation while requiring substantially fewer comparisons.", "body_md": "arXiv:2608.11704v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time", "canonical_source": "https://www.machinebrief.com/news/robust-and-efficient-noisy-label-time-series-classification-qmyt", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:41:46.573539+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time", "markdown": "https://wpnews.pro/news/robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time.md", "text": "https://wpnews.pro/news/robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time.txt", "jsonld": "https://wpnews.pro/news/robust-and-efficient-noisy-label-time-series-classification-via-dynamic-time.jsonld"}}