aiqbench analysis
This research introduces DTW-GBC, a method that enhances the robustness and inference speed of time-series classifiers by organizing data into granular balls to resist label noise. By reducing computational comparisons during inference while maintaining accuracy in noisy environments, the approach offers a practical optimization for real-world machine learning applications involving temporal data. The study validates these gains across multiple benchmark datasets, demonstrating a strong balance between efficiency and resilience against training data imperfections.
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
arXiv:2608.11704v2 Announce Type: replace-cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.11704 [cs.LG]
(or arXiv:2608.11704v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.11704 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 202 words
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