Active Anomaly Detection Technology Based on Ensemble Learning  

作　　者：Weiwei Liu Shuya Lei Liangying Peng Jun Feng Sichen Pan Meng Gao 

机构地区：[1]Artificial Intelligence On Electric Power System State Grid Corporation Joint Laboratory,State Grid Smart Grid Research Institute Co.Ltd.,Beijing 102209,China [2]State Grid Zhejiang Information and Telecommunication Branch,Hangzhou 310016,China [3]Harbin Institute of Technology,Harbin 150001,China

出　　处：《国际计算机前沿大会会议论文集》2022年第1期53-66,共14页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：supported by the State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).

摘　　要：Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.

关 键 词：Anomaly detection Ensemble learning Artificial anomaly detection Methods to reduce labor cost Model self-training 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]