A drift aware adaptive method based on minimum uncertainty for anomaly detection in social networking

作者：

Highlights：

• We present concept drift detection based on online learning algorithms.

• Our approach provides an adaptive learning system for anomaly detection in data stream.

• Minimum uncertainty employed to combining online learning algorithm.

• Anomaly in social network data stream employed as a real world concept drift benchmark.

摘要

•We present concept drift detection based on online learning algorithms.•Our approach provides an adaptive learning system for anomaly detection in data stream.•Minimum uncertainty employed to combining online learning algorithm.•Anomaly in social network data stream employed as a real world concept drift benchmark.

论文关键词：Concept drift,Data stream,Fusion of experts,Online learning

论文评审过程：Received 16 July 2019, Revised 4 August 2020, Accepted 11 August 2020, Available online 19 August 2020, Version of Record 10 October 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113881