计算机应用研究2024，Vol.41Issue(1)：88-93,6.DOI:10.19734/j.issn.1001-3695.2023.05.0182
基于专家反馈的广义孤立森林异常检测算法
Generalized isolation forest anomaly detection algorithm based on expert feedback
摘要
Abstract
Aiming at the problem that the isolation forest algorithm cannot detect local anomalies parallel to the axes and the tree structure is unable to be dynamically updated,this paper proposed a generalized isolation forest anomaly detection algo-rithm based on expert feedback.Firstly,it projected the data to the sampled normal unit vector,and selected a split point from the mapping area to divide the data space,then repeated these operations until constructed a generalized isolation tree.Second-ly,it introduced the weights of the leaf nodes of each tree in the generalized isolation forest,which comprehensively considered the influence of the number of subspace partitions and the sample size in the subspace on anomaly scores.Finally,it calculated the weighted anomaly scores of each data,and submitted data with high anomaly scores to expert for batch labeling,then the al-gorithm updated the weights of the leaf nodes according to the labeling results,so as to dynamically adjust the structure of the generalized isolation tree.The experimental results show that the numbers of real abnormal data are marked by expert in 7 data-sets are better than that of the other tree-based anomaly detection algorithms,and the average precision in 12 datasets are 38.952％,49.144％and 49.144％higher than isolation forest,extended isolation forest,generalized isolation forest,respectively.关键词
异常检测/孤立森林/动态更新/专家反馈Key words
anomaly detection/isolation forest/dynamic update/expert feedback分类
信息技术与安全科学引用本文复制引用
祝诚勇,黄鹏翔,李理敏..基于专家反馈的广义孤立森林异常检测算法[J].计算机应用研究,2024,41(1):88-93,6.基金项目
国家自然科学基金面上项目(61972288) （61972288）
浙江省教育厅科研项目(Y202146796) （Y202146796）
温州市重大科技创新攻关项目(ZG2021029) （ZG2021029）
