摘要
The cigarette detection data contains a large amount of true sample data and a small amount of false sample data. The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the identification of real and fake cigarettes. Binary particle swarm optimization algorithm is used to improve the isolation forest construction process, and isolation trees with high precision and large differences are selected, which improves the accuracy and efficiency of the algorithm. The distance between the obtained anomaly score and the clustering center of the k-means algorithm is used as the threshold for anomaly judgment. The experimental results show that the accuracy of the BPSO-iForest algorithm is improved compared with the standard iForest algorithm. The experimental results of multiple brand samples also show that the method in this paper can accurately use the detection data for authenticity identification.
The cigarette detection data contains a large amount of true sample data and a small amount of false sample data. The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the identification of real and fake cigarettes. Binary particle swarm optimization algorithm is used to improve the isolation forest construction process, and isolation trees with high precision and large differences are selected, which improves the accuracy and efficiency of the algorithm. The distance between the obtained anomaly score and the clustering center of the k-means algorithm is used as the threshold for anomaly judgment. The experimental results show that the accuracy of the BPSO-iForest algorithm is improved compared with the standard iForest algorithm. The experimental results of multiple brand samples also show that the method in this paper can accurately use the detection data for authenticity identification.
作者
Yupeng Xu
Hao Dong
Mingzhu Zhou
Jun Xing
Xiaohui Li
Jian Yu
Yupeng Xu;Hao Dong;Mingzhu Zhou;Jun Xing;Xiaohui Li;Jian Yu(China National Tobacco Quality Supervision and Test Center, Zhengzhou, China;Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;University of Science and Technology of China, Hefei, China)