混合采样和集体学习为食品安全采样检查分类分类
Ke Qin1, Xiaoting Dai2, Linhai Wu2
1School of Business, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, PR China.
Journal of food protection
|October 25, 2025
概括
一种新的混合采样方法,LOF-KNN-CSENN,通过减少噪音和保持边界,有效地平衡食品安全数据. 结合集体学习,它显著改善了不合格食品样本的检测.
科学领域:
- 食品安全 食品安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 食品安全采样中的极端阶级失衡使ML模型偏向于检测不合格的样本.
- 传统的过量采样方法与复杂的食品检查数据作斗争,导致检测不良.
- 处理非线性特征,复杂分布和多类场景对于准确的食品安全分析至关重要.
研究的目的:
- 开发一种新的混合采样算法,以克服食品安全检查中传统方法的局限性.
- 通过减轻阶级不平衡和提高模型稳定性来提高不合格食品样本的检测.
- 通过使用先进的ML技术,引入食品安全监管的智能框架.
主要方法:
- 拟议的LOF-KNN-CSENN:一种混合算法,将SMOTE和ENN与LOF用于噪声过和KNN用于边界保护相结合.
- 实现了一个堆叠集体学习框架,使用六个基于树的模型和后勤回归 (LR) 作为元模型.
- 使用Shapley添加剂解释 (SHAP) 识别食品安全的关键风险因素.
主要成果:
- LOF-KNN-CSENN有效地抑制了噪音样本合成和均衡的数据分布在食品检查数据集.
- 与单个模型相比,集成堆叠组合模型实现了更高的精度 (0.4-5.6%) 和F1得分 (0.8-30.7%).
- SHAP分析确定生产地址,采样阶段和位置是针对性食品安全监督的关键风险因素.
结论:
- 拟议的LOF-KNN-CSENN算法和堆叠组合框架为智能食品安全监管提供了强大的解决方案.
- 这种方法通过解决阶级不平衡,显著提高了在多类食品检查中检测不合格样本的能力.
- 这些发现支持有针对性的监督策略,突出了关键的风险因素,有助于改善食品安全结果.
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