Safety Risk Level Early Warning of Meat Products Based on Optimized Ensemble Learning
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Abstract:
Five safety risk levels were established based on the detection data of meat products sampled between 2013 and 2017. Feature construction and one-hot encoding were used to further correlate factors relevant to meat product safety. An extreme gradient boosting (XGBOOST) model was established to study the influence levels of various factors during food production on the safety risk level; subsequently then multiple indices were used to evaluate the model. In addition, sample imbalance problem was solved by upsampling, and the hyperparameters were adjusted by Bayesian optimization to improve the model performance and classification results. Simultaneously, the model constructed was compared with the decision tree (DT) and random forest (RF) methods to evaluate their classification performance. The XGBOOST outperformed others in classifying the safety risk levels of meat products. Food production processes are complex, and this study shows that model processing with one-hot encoding could effectively identify the influence levels of various factors on food safety. Moreover, the result suggested that XGBOOST performs better in terms of total accuracy and other indices after parameter adjustment, compared to the RF model, while RF had the most stable learning performance. The average of accuracy and F1 score can reach 89.14% and 88.59%, respectively, under different sampling. The results suggest that XGBOOST can be applied to determine safety risk levels of meat products and provide technical support for daily supervision.
