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Jinzong Dong

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AAAI Conference 2025 Conference Paper

Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling

Jinzong Dong
Zhaohui Jiang
Dong Pan
Haoyang Yu

Confidence calibration of classification models is a technique to estimate the true posterior probability of the predicted class, which is critical for ensuring reliable decision-making in practical applications. Existing confidence calibration methods mostly use statistical techniques to estimate the calibration curve from data or fit a user-defined calibration function, but often overlook fully mining and utilizing the prior distribution behind the calibration curve. However, a well-informed prior distribution can provide valuable insights beyond the empirical data under the limited data or low-density regions of confidence scores. To fill this gap, this paper proposes a new method that integrates the prior distribution behind the calibration curve with empirical data to estimate a continuous calibration curve, which is realized by modeling the sampling process of calibration data as a binomial process and maximizing the likelihood function of the binomial process. We prove that the calibration curve estimating method is Lipschitz continuous with respect to data distribution and requires smaller sample sizes than histogram binning. Also, a new calibration metric has been designed, leveraging the estimated calibration curve to estimate the true calibration error, and it has been proven to be a consistent calibration measure. Furthermore, realistic calibration datasets can be generated by the binomial process modeling from a preset true calibration curve and confidence score distribution, which can serve as a benchmark to measure and compare the discrepancy between existing calibration metrics and the true calibration error. The effectiveness of our calibration method and metric are verified in real-world and simulated data. We believe our exploration of integrating prior distributions with empirical data will guide the development of better-calibrated models, contributing to trustworthy AI.

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EAAI Journal 2023 Journal Article

A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification

Zhaohui Jiang
Jinzong Dong
Dong Pan
Tianyu Wang
Weihua Gui

Determining the taphole closing time is an essential task in the blast furnace ironmaking process because the closing time directly affects the efficiency of iron production and the stability of the blast furnace. However, at present, the taphole closing time in most ironmaking plants is judged by on-site workers based on experience, which lacks scientific guidance. To determine the taphole closing time intelligently and accurately, a novel monitoring method is proposed, which innovatively simplifies the monitoring problem of the absolute taphole closing time into a two-stage classification problem of relative tapping state. In the first stage, a classification algorithm SE-ResNeXt, which only takes the molten iron flow image data as the input data, is used to preliminarily determine the current molten iron flow state in the time dimension during tapping. When it is recognized that the molten iron flow is in the last tapping state in the first stage, the second stage is carried out. In the second stage, a novel multimodal data fusion network SENeXt-Decoder consisting of a novel image feature extraction module, a novel fusion module and a multi-head attention decoder is proposed to obtain the exact taphole closing time, which fuses the molten iron flow image data and blast furnace operating state data. The comparison experiment with the actual taphole closing time on site shows that the absolute monitoring error of this method is within 120 s, and the relative monitoring error is within 1. 2%, which better meets the factory’s demand for error accuracy.

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