Uncertainty-Aware Multiple Instance Learning fromLarge-Scale Long Time Series Data

• URL: http://arxiv.org/abs/2111.08625v2
• Date: Wed, 17 Nov 2021 19:11:47 GMT
• Title: Uncertainty-Aware Multiple Instance Learning fromLarge-Scale Long Time Series Data
• Authors: Yuansheng Zhu, Weishi Shi, Deep Shankar Pandey, Yang Liu, Xiaofan Que, Daniel E. Krutz, and Qi Yu
• Abstract summary: This paper proposes an uncertainty-aware multiple instance (MIL) framework to identify the most relevant periodautomatically. We further incorporate another modality toaccommodate unreliable predictions by training a separate model and conduct uncertainty aware fusion. Empirical resultsdemonstrate that the proposed method can effectively detect thetypes of vessels based on the trajectory.
• Score: 20.2087807816461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel framework to classify large-scale time series data with long duration. Long time seriesclassification (L-TSC) is a challenging problem because the dataoften contains a large amount of irrelevant information to theclassification target. The irrelevant period degrades the classifica-tion performance while the relevance is unknown to the system.This paper proposes an uncertainty-aware multiple instancelearning (MIL) framework to identify the most relevant periodautomatically. The predictive uncertainty enables designing anattention mechanism that forces the MIL model to learn from thepossibly discriminant period. Moreover, the predicted uncertaintyyields a principled estimator to identify whether a prediction istrustworthy or not. We further incorporate another modality toaccommodate unreliable predictions by training a separate modelbased on its availability and conduct uncertainty aware fusion toproduce the final prediction. Systematic evaluation is conductedon the Automatic Identification System (AIS) data, which is col-lected to identify and track real-world vessels. Empirical resultsdemonstrate that the proposed method can effectively detect thetypes of vessels based on the trajectory and the uncertainty-awarefusion with other available data modality (Synthetic-ApertureRadar or SAR imagery is used in our experiments) can furtherimprove the detection accuracy.

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