基于多维时空风险特征工程的食用农产品抽检结果预测模型研究

Research on predictive model for edible agricultural product inspection results based on multidimensional spatiotemporal risk feature engineering

中文关键词:  食用农产品  多维时空风险特征工程  轻量级梯度提升机  预测模型

英文关键词:edible agricultural products  multidimensional spatiotemporal risk feature engineering  light gradient boosting machine  predictive model

中文摘要:

      目的 建立基于多维时空风险特征工程的机器学习预测模型, 对食用农产品监督抽检结果进行预测。方法 基于2022—2024年福建省地级市食用农产品监督抽检数据, 通过系统性地整合数据的食用农产品品种、抽样地、抽样场所、生产日期等信息, 引入多维时空风险特征工程方法处理数据集, 在原始数据的基础上构建6种新特征组成新数据集, 同时运用3种主流的预测模型对处理后的数据进行食用农产品抽检结果预测。结果 在3种主流模型上, 多维时空风险特征工程方法的预测性能均优于传统采样方法。其中, 多维时空风险特征工程与轻量级梯度提升机(light gradient boosting machine, LightGBM)组合对测试集样本的抽检结果进行预测, 该模型AUC值(0.845)、Recall (0.881)、G-mean值(0.741)、业务成本(8561), 综合性能最佳。结论 多维时空风险特征工程相较于传统采样方法, 增强了模型对复杂风险模式的捕捉能力, 使模型的预测性能得到全面提升。基于多维时空风险特征工程的LightGBM模型能够较为准确地预测食用农产品的抽检结果, 能够为差异化、动态化的食用农产品监督抽检方案制定及食用农产品安全风险预警提供技术支撑。

英文摘要:

      Objective To establish a machine learning predictive model based on multidimensional spatiotemporal risk feature engineering for forecasting the results of supervisory sampling inspections of edible agricultural products. Methods Using supervisory sampling data for edible agricultural products from Fujian’s prefecture-level cities between 2022 and 2024, information on product type, sampling location, sampling site and production date were systematically integrated. Multidimensional spatiotemporal risk feature engineering was applied to process the dataset, constructing 6 kinds of new features to form a novel dataset based on the original data. The 3 kinds of mainstream binary classification prediction models were applied to forecast inspection outcomes using the processed data. Results Across all 3 kinds of mainstream models, the multidimensional spatiotemporal risk feature engineering method demonstrated superior predictive performance compared to traditional sampling methods. Specifically, the combination of multidimensional spatiotemporal risk feature engineering with light gradient boosting machine (LightGBM) achieved the best overall performance on the test set samples, with an AUC of 0.845, Recall of 0.881, G-mean of 0.741 and misclassification costs of 8561. Conclusion Compared to traditional sampling methods, multidimensional spatiotemporal risk feature engineering enhances the model’s ability to capture complex risk patterns, leading to a comprehensive improvement in predictive performance. The LightGBM model based on multidimensional spatiotemporal risk feature engineering can accurately predict the inspection results of edible agricultural products, providing technical support for formulating differentiated, dynamic supervision and inspection plans and risk early warning systems for edible agricultural products.