| 引用本文: | 刘光惠1 ,陈卓超2 ,鲜思东2 ,冯苗苗2 ,鲜智宇3 ,李常郡4.基于随机森林的食品安全监测预警研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（5）：27-35 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 基于随机森林的食品安全监测预警研究 |
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刘光惠1 ,陈卓超2 ,鲜思东2 ,冯苗苗2 ,鲜智宇3 ,李常郡4
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1. 重庆市市场监督管理局 档案信息中心,重庆 400014
2. 重庆邮电大学 理学院,重庆 400065
3. 南开大学 物理学院,天津 300071
4. 东南大学 吴健雄学院,南京 211102
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| 摘要: |
| 目的 对商户进行食品安全监测预警是我国食品安全监管中的热点问题,实现高效的食品安全预警与监管。
方法 结合重庆地区食品安全的特点, 采用专家评价的方式得到综合评价指标,提出 AHP-BP 神经网络综合评价,
建立了具有可靠、客观的食品安全综合评价体系,在此基础上,动态挖掘食品安全特征指数,给出食品安全综合画
像,通过建立基于随机森林的重庆食品安全预警监测的模型,并对重庆市江北区等食品安全数据进行仿真验证。
结果 综合评价指标体系更加合理,预测的准确率、运行时间等相较 XGBoost 算法均有明显提升。 结论 机器学习方
法有助于建立更完备、合理的食品安全评价体系,基于随机森林的食品安全预警模型在精确率、AUC、召回率等方
面表现更优, 在商户食品安全监管中不仅精度高,还有很好的鲁棒性。 |
| 关键词: 食品安全 评价体系 随机森林 XGBoost 算法 特征指数 |
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| Research on Food Safety Monitoring and Early Warning Based on Random Forest |
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LIU Guanghui
1
CHEN Zhuochao
2
XIAN Sidong
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FENG Miaomiao
2
XIAN Zhiyu
3
LI Changjun
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1. Information Center Chongqing Market Supervision Administration Chongqing 400014 China
2. School of Science Chongqing University of Posts and Telecommunications Chongqing 400065 China
3. School of Physics Nankai University Tianjin 300071 China
4. Chien-Shiung Wu College Southeast University Nanjing 211102 China
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| Abstract: |
| Objective Conducting food safety monitoring and early warning for merchants is a critical issue in China?? s food
safety supervision. This study aims to achieve efficient food safety early warning and supervision. Methods In
combination with the characteristics of food safety in Chongqing comprehensive evaluation indicators were obtained
through expert assessment. An AHP-BP neural network comprehensive evaluation method was proposed and a reliable
and objective food safety evaluation system was established. Based on this dynamic mining of food safety feature indices
was conducted to generate a comprehensive food safety profile. A food safety early warning and monitoring model based on
random forest was established and the model was verified through simulation using food safety data from Jiangbei District
Chongqing. Results The comprehensive evaluation indicator system is more rational with significant improvements in
prediction accuracy and runtime compared with the XGBoost algorithm. Conclusion Machine learning methods contribute
to the establishment of a more comprehensive and rational food safety evaluation system. The food safety early warning
model based on random forest performs better in terms of precision AUC and recall rate. It not only achieves high
accuracy in food safety supervision for merchants but also demonstrates good robustness. |
| Key words: food safety evaluation system random forest XGBoost algorithm feature index |
