基于堆叠模型融合的关键人员食品犯罪预测模型研究

Yupeng Zhai, X. Li, Kang Wang
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引用次数: 0

摘要

犯罪预测对重大事件的食品安全防范工作具有重要意义。传统的犯罪预测依靠警务人员的经验,具有很强的主观性,无法提前预测。本文对食品安全警务数据进行分析处理,结合食品犯罪的特点,采用堆叠模型融合方法预测关键人员的食品犯罪倾向,并通过召回率对模型进行验证。结果表明,该综合学习模型具有较高的准确率,能够有效预测关键人员的食品犯罪倾向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Prediction Model of Key Personnel's Food Crime Based on Stacking Model Fusion
Crime prediction is of great significance to the food safety defense work of major events. The traditional crime prediction depends on the experience of police officers, which is highly subjective and can not be predicted in advance. This paper analyzes and processes food safety police data, combines the characteristics of food crimes, uses stacking model fusion method to predict the food crime tendency of key personnel, and verifies the model through the Recall Rate. The results show that the integrated learning model has high accuracy and can effectively predict the food crime tendency of key personnel.
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