Active Anomaly Detection Technology Based on Ensemble Learning

Abstract

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.