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Risk warning based on GA-improved stacking ensemble learning algorithm: a case study of alcohol

Annals of Operations Research 2025年 1-28页

作者： Song, Cen Shen, Hanwen Zhuang, Jun School of Economics and Management China University of Petroleum No. 18 Fuxue Road Changping District Beijing 102249 China Department of Industrial and Systems Engineering University at Buffalo Buffalo 14260 NY United States

As one of the important daily consumer goods, alcoholic beverage presents high safety risks and potential hazards. Therefore, ensuring its quality and safety to meet consumer demand is urgent. Meanwhile, in response to the iterative and optimization issues in ensemble learning, this paper introduces a Genetic algorithm (GA) to enhance the performance of the Stacking ensemble learning model. Based on the inspection data of alcoholic food products published by National Food Safety Sampling Inspection Results Query System in China, this study applies the Synthetic Minority Oversampling Technique and Tomek Links algorithm for comprehensive sampling, to achieve balanced categorization of different samples. It effectively mitigates the impact of non-smooth data and improves the classification results biased towards majority class samples. After cross-validation and hyperparameter optimization, the proposed GA-improved Stacking ensemble learning model has an accuracy of 0.885, a precision of 0.882, a recall of 0.885, and an F1 score of 0.876, comparing with traditional single classifier algorithms such as random search, Bayesian search, and other methods. This study provides an effective risk warning method for regulatory agencies' inspection focus. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Ensemble learning Genetic algorithm Risk warning single classifier algorithm

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