Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data
Yuansheng Zhu, Weishi Shi, Deep Shankar Pandey, Yang Liu, Xiaofan Que, Daniel E. Krutz, Qi Yu · 2021 IEEE International Conference on Big Data (Big Data) · 2021
We propose a novel framework to classify large-scale time series data with long duration. Long time series classification (L-TSC) is a challenging problem because the data often contains a large amount of irrelevant information to the classification target. The irrelevant period degrades the classification performance while the relevance is unknown to the system. This paper proposes an uncertainty-aware multiple instance learning (MIL) framework to identify the most relevant period automatically. The predictive uncertainty enables designing an attention mechanism that forces the MIL model to learn from the possibly discriminant period. Moreover, the predicted uncertainty yields a principled estimator to identify whether a prediction is trustworthy or not. We further incorporate another modality to accommodate unreliable predictions by training a separate model based on its availability and conduct uncertainty aware fusion to produce the final prediction. Systematic evaluation is conducted on the Automatic Identification System (AIS) data, which is collected to identify and track real-world vessels. Empirical results demonstrate that the proposed method can effectively detect the types of vessels based on the trajectory and the uncertainty-aware fusion with other available data modality (Synthetic-Aperture Radar or SAR imagery is used in our experiments) can further improve the detection accuracy.