Improved Isolation Forest Algorithm for Anomaly Test Data Detection  

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)

机构地区：[1]China National Tobacco Quality Supervision and Test Center, Zhengzhou, China [2]Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China [3]University of Science and Technology of China, Hefei, China

出　　处：《Journal of Computer and Communications》2021年第8期48-60,共13页电脑和通信（英文）

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.

关 键 词：Isolation Forest BPSO K-Means Cluster Anomaly Detection 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]