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10 November 2020 Coverage-driven deep prediction intervals method
Zhiwei Huang, Bowen Hu, Hao He, Yaohui Jin
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Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841O (2020) https://doi.org/10.1117/12.2579788
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China
Abstract
Prediction Intervals (PIs) provide a method to quantify the uncertainty of deep neural networks' point forecasts. High-quality PIs should be as narrow as possible while covering a designated percentage of data points, which is an axiomatic theory. Lower Upper Bound Estimation (LUBE) method is the first to incorporate this axiom into the neural networks loss function, however it pays much attention to the interval width, and thus the coverage probability does not achieve the desired result. Consequently, the PIs are unreliable and their practical application risks are elevated. In this paper, prioritizing coverage probability, we propose a coverage-driven approach that is generalized to any neural networks model, combining bootstrap method with improved LUBE method. We show that PIs constructed by our method are more reliable, and model uncertainty is quantified using bootstrap. Moreover, compared with two novel PI methods, benchmark experiments show our method is able to reduce the mean PI width by more than 7.5% while obtaining the better results in coverage probability.
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Zhiwei Huang, Bowen Hu, Hao He, and Yaohui Jin "Coverage-driven deep prediction intervals method", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841O (10 November 2020); https://doi.org/10.1117/12.2579788
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KEYWORDS
Data modeling
Neural networks
Reliability
Performance modeling
Statistical analysis
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