An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift.

ABSTRACT: A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10817223 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature