Application Research of Machine Learning in Food Safety Risk Early Warning and Sampling Inspection Program

Abstract

Abstract: The problem-oriented sampling inspection program of food safety supervision plays an extremely important role in strengthening food safety risk management. Existing researches on food sampling data rarely consider the unbalanced characteristics of unqualified samples. Based on the food inspection and sampling data of Xinjiang from 2015 to 2017, this paper introduces the solution of unbalanced problems into the research on the construction of early warning models of food safety risks, and constructs a combined algorithm model of multiple sampling methods, cost-sensitive methods and SVM, and random forest . Our analysis and comparison show that sampling methods are inferior to cost-sensitive method in solving the problem of unbalanced data in food sampling. Regarding the construction of the risk warning model, although the cost-sensitive + SVM combination performance is slightly better than the cost-sensitive + random forest combination, the running time of the former is much longer than the latter. The empirical results show that the combined algorithm model based on imbalanced data solution can effectively improve the early warning effect of food safety risks, and provide decision support for formulating scientific and reasonable sampling inspection program of food safety supervision and improving the effectiveness of food safety risk management.

Key words: supervision and sampling inspection, imbalance problem, combined algorithm model, risk warning

Yang Hongyan, Tian Yingjie. Application Research of Machine Learning in Food Safety Risk Early Warning and Sampling Inspection Program[J]. Management Review, 2022, 34(11): 315-323.

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