Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing

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.

arXiv:2608.11704v1 Announce Type: new Abstract: 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.