Improved Isolation Forest Algorithm for Anomaly Test Data Detection

Yupeng Xu; Hao Dong; Mingzhu Zhou; Jun Xing; Xiaohui Li; Jian Yu

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Improved Isolation Forest Algorithm for Anomaly Test Data Detection

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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.

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来源
《电脑和通信（英文）》 |2021年第8期|P.48-60|共13页
作者
Yupeng Xu; Hao Dong; Mingzhu Zhou; Jun Xing; Xiaohui Li; Jian Yu;
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作者单位

China National Tobacco Quality Supervision and Test Center Zhengzhou China;

China National Tobacco Quality Supervision and Test Center Zhengzhou ChinaHefei Institutes of Physical Science Chinese Academy of Sciences Hefei ChinaUniversity of Science and Technology of China Hefei China;

China National Tobacco Quality Supervision and Test Center Zhengzhou China;

China National Tobacco Quality Supervision and Test Center Zhengzhou China;

China National Tobacco Quality Supervision and Test Center Zhengzhou China;

China National Tobacco Quality Supervision and Test Center Zhengzhou China;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
Isolation Forest; BPSO; K-Means Cluster; Anomaly Detection;

Improved Isolation Forest Algorithm for Anomaly Test Data Detection

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