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基于特征选择的食品掺杂物可视分析系统

Visual Analysis System for Food Adulterants Based on Feature Selection

  • 摘要: 食品安全抽检数据中蕴含丰富的信息, 这些信息在食品安全早期预警、风险预测等方面发挥巨大贡献.为深入研究食品类别中的高权重掺杂物, 本文基于特征选择技术设计了一个基于对比学习的分类问题, 结合特征之间的相关性度量, 设计并实现了一个掺杂物关联分析可视化系统.该系统针对样本分类结果、掺杂物特征权重、掺杂物特征相关性和模型评估指标设计并实现了多视图及之间的联动交互, 支持用户根据上述信息迭代式分析得到最优特征组合.最后, 结合真实食品安全检测数据集证明该系统可以通过自动化的方式更加方便、直接地获取食品的掺杂物权重, 增强掺杂物特征组合.

     

    Abstract: Food safety inspection data contains rich information, which plays a great contribution in early warning and risk prediction of food safety. To deeply study the high-weight adulterants in food categories, we designed a classification problem based on contrast learning based on feature selection techniques, combined with correlation metrics between features, and finally implemented an adulterant correlation analysis visualization system. The system is designed and implemented with multiple views and linkage interactions for sample classification results, adulterant feature weights, adulterant feature correlations and model evaluation metrics, and can support users to get the optimal feature combinations based on the above information in an iterative analysis. Finally, we combine a real food safety testing dataset to demonstrate that the system can be automated to obtain adulterant weights of food products more easily and directly, and enhance the adulterant feature combinations.

     

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