文章基本信息

标题：Improved Isolation Forest Algorithm for Anomaly Test Data Detection
本地全文：下载
作者：Yupeng Xu ; Hao Dong ; Mingzhu Zhou 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2021
卷号：9
期号：8
页码：48-60
DOI：10.4236/jcc.2021.98004
语种：English
出版社：Scientific Research Publishing
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.
关键词：Isolation Forest;BPSO;K-Means Cluster;Anomaly Detection